library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(meta)
## Loading 'meta' package (version 6.5-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drs
library(PRISMAstatement)
library(skimr)
library(MASS)
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
library(ggpubr)
setwd("~/Desktop/Chapter 4")
photo <- read.csv("observations_2022.csv")
summary(photo)
## region site site_code microsite
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month day
## Min. :1.000 Min. :1.000 Length:58015 Min. : 3.00
## 1st Qu.:1.000 1st Qu.:1.000 Class :character 1st Qu.: 6.00
## Median :1.000 Median :2.000 Mode :character Median :16.00
## Mean :1.775 Mean :1.527 Mean :13.98
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:22.00
## Max. :4.000 Max. :2.000 Max. :29.00
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:58015
## 1st Qu.:2022 1st Qu.: 0.000 1st Qu.: 5386 Class :character
## Median :2022 Median : 0.000 Median :12638 Mode :character
## Mean :2022 Mean : 4.681 Mean :15170
## 3rd Qu.:2022 3rd Qu.:11.000 3rd Qu.:24318
## Max. :2022 Max. :14.000 Max. :38822
## filename timestamp animal.hit class
## Length:58015 Length:58015 Min. :0.00000 Length:58015
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.06521
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:58015 Min. : 1.000
## Class :character 1st Qu.: 1.000
## Mode :character Median : 1.000
## Mean : 1.001
## 3rd Qu.: 1.000
## Max. :12.000
photo <- photo %>%
filter(common_name != "Human")
photo <- photo %>%
filter(common_name != "Human-Camera Trapper")
photo <- photo %>%
filter(common_name != "Domestic Dog")
photo <- photo %>%
filter(common_name != "Vehicle")
photo <- photo %>%
dplyr::filter(common_name != "Insect")
photo <- photo %>%
dplyr::filter(common_name != "Animal")
photo <- photo %>%
dplyr::filter(common_name != "Bird")
photo <- photo %>%
filter(common_name != "No CV Result")
count.hit <- photo %>%
count(animal.hit) %>%
na.omit()
summary(count.hit)
## animal.hit n
## Min. :0.00 Min. : 3169
## 1st Qu.:0.25 1st Qu.:15935
## Median :0.50 Median :28700
## Mean :0.50 Mean :28700
## 3rd Qu.:0.75 3rd Qu.:41466
## Max. :1.00 Max. :54232
### 2022 Had a 5.88% catch rate
### Animal Observations by Site_Code
animals_by_sitecode <- photo%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode <- animals_by_sitecode %>%
filter(common_name != "Blank")
### Animal observations by Site
animals_by_site <- photo %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site <- animals_by_site %>% filter(common_name != "Blank")
### Animal observations by Density
animals_by_density <- photo %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density <- animals_by_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Total Observations 2022
Total_Observations <- photo %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
density_obvs <- merge(animals_by_density, Total_Observations, all = TRUE)
density_obvs$percent_presence <- density_obvs$captures/density_obvs$total
### Percent proportion Figure
plot1 <- ggplot(density_obvs, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot1 + scale_fill_manual(values = c("#009900", "#0066cc"))
library(emmeans)
m1 <- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs)
anova(m1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 44 22806
## microsite 1 5.4 43 22801 0.01984 *
## common_name 26 22800.9 17 0 < 2e-16 ***
## microsite:common_name 17 0.0 0 0 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e1 <- emmeans(m1, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
#head(e1)
animals_density <- photo %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density <- animals_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### PCA
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-4
library(ape) ### For PCOA
##
## Attaching package: 'ape'
## The following object is masked from 'package:ggpubr':
##
## rotate
## The following object is masked from 'package:dplyr':
##
## where
pca_data <- animals_density ### Created new df for pca data
pca_data <- pca_data %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data)
## [1] 22 28
env <- read.csv("environment.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22 5
m01 <- adonis(pca_data ~ microsite*shrub_density, data = env)
## 'adonis' will be deprecated: use 'adonis2' instead
m01
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2242 0.22425 0.76358 0.03600 0.592
## shrub_density 1 0.4245 0.42452 1.44550 0.06815 0.199
## Residuals 19 5.5799 0.29368 0.89584
## Total 21 6.2287 1.00000
##
## $call
## adonis(formula = pca_data ~ microsite * shrub_density, data = env)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -4.757692 5.000000e-02 -13.455128
## microsite1 -6.857692 -5.000000e-02 -18.955128
## shrub_density 1.876923 1.823528e-17 4.184615
## microsite1:shrub_density NA NA NA
## Blunt-nosed Leopard Lizard Bobcat
## (Intercept) 0.9987179 -0.6602564
## microsite1 0.8987179 -1.1602564
## shrub_density -0.1538462 0.1692308
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) 0.18717949 20.411538
## microsite1 -0.21282051 13.511538
## shrub_density 0.06153846 -1.815385
## microsite1:shrub_density NA NA
## California Pocket Mouse California Quail
## (Intercept) 0.35641026 -4.2487179
## microsite1 0.15641026 -5.6487179
## shrub_density -0.03076923 0.9538462
## microsite1:shrub_density NA NA
## California Thrasher Common Raven Coyote
## (Intercept) -1.4743590 0.7910256 1.4923077
## microsite1 -1.4743590 -3.5089744 -4.1076923
## shrub_density 0.2769231 0.3230769 0.4769231
## microsite1:shrub_density NA NA NA
## Desert Cottontail Desert Iguana Giant Kangaroo Rat
## (Intercept) -24.729487 2.07820513 6.9935897
## microsite1 -26.629487 -1.82179487 4.4935897
## shrub_density 4.861538 -0.01538462 -0.7692308
## microsite1:shrub_density NA NA NA
## Great White Egret Greater Roadrunner
## (Intercept) 5.000000e-02 -4.3730769
## microsite1 -5.000000e-02 -4.4730769
## shrub_density 1.076807e-17 0.8307692
## microsite1:shrub_density NA NA
## Heermann's Kangaroo Rat Killdeer Kit Fox
## (Intercept) 140.330769 5.000000e-02 0.44487179
## microsite1 59.130769 -5.000000e-02 0.14487179
## shrub_density -7.707692 6.937479e-18 -0.01538462
## microsite1:shrub_density NA NA NA
## Lark Sparrow Loggerhead Shrike Mohave Ground Squirrel
## (Intercept) -1.8384615 -0.5935897 0.81923077
## microsite1 -2.2384615 -0.8935897 0.71923077
## shrub_density 0.3846154 0.1692308 -0.09230769
## microsite1:shrub_density NA NA NA
## Mourning Dove Nelson's Antelope Squirrel
## (Intercept) -1.9384615 18.984615
## microsite1 -2.1384615 13.784615
## shrub_density 0.3846154 -2.246154
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Vesper Sparrow
## (Intercept) -0.5641026 5.000000e-02 5.000000e-02
## microsite1 -0.5641026 -5.000000e-02 -5.000000e-02
## shrub_density 0.1076923 1.076807e-17 6.937479e-18
## microsite1:shrub_density NA NA NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.72187241 0.84634032 1.11978420 0.78585052
## microsite1 0.19194029 0.33115230 0.42744428 0.26477687
## shrub_density -0.03063205 -0.05640075 -0.06512504 -0.03711672
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.7805666 0.70692759 0.95262164 0.80543797
## microsite1 0.2293742 0.15739580 0.22071669 0.25186788
## shrub_density -0.0393115 -0.02414532 -0.02857079 -0.03727318
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7428122 1.4970441 0.99852391 1.06366871
## microsite1 1.0220992 0.8596173 0.44864549 0.53735308
## shrub_density -0.1847971 -0.1548735 -0.07052619 -0.08656954
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.82385406 0.795551518 0.79238103 0.84065136
## microsite1 0.12690322 0.021850057 0.21124835 0.30308675
## shrub_density -0.02568071 -0.008853955 -0.02915258 -0.04325977
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.38555800 0.50980072 0.68786923 0.55515661
## microsite1 -0.59216512 -0.43255379 -0.20343288 -0.30788751
## shrub_density 0.09323238 0.06736799 0.02712249 0.04455634
## microsite1:shrub_density NA NA NA NA
## 21 22
## (Intercept) 0.881696341 0.64736295
## microsite1 0.013954406 -0.20302121
## shrub_density -0.001421805 0.02954886
## microsite1:shrub_density NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.51232234 2.473580584
## [2,] 0.49704227 1.354522877
## [3,] 0.52697747 2.999162560
## [4,] 0.50159378 1.112610936
## [5,] 0.35748234 0.433008035
## [6,] 0.81978454 1.143168562
## [7,] 0.59669034 0.268670214
## [8,] 1.00688936 1.029652890
## [9,] 1.15780782 0.858601644
## [10,] 3.89781386 0.644123834
## [11,] 0.52030537 1.698905230
## [12,] 1.62795975 0.978406120
## [13,] 1.00678867 0.844647685
## [14,] 0.53481867 0.176443763
## [15,] 1.44174975 0.594217705
## [16,] 0.61611860 0.989803128
## [17,] 0.91864835 0.286766250
## [18,] 2.64497631 0.673405539
## [19,] 0.42536604 0.681951346
## [20,] 1.73878143 0.374036871
## [21,] 1.10221417 2.093689181
## [22,] 0.63496049 0.509472870
## [23,] 1.09809428 1.196762517
## [24,] 2.21326320 0.832747600
## [25,] 0.51845280 0.329327758
## [26,] 0.68178433 0.978852789
## [27,] 1.00707132 0.852385361
## [28,] 2.65152874 1.577666803
## [29,] 0.71714657 0.377430552
## [30,] 1.29562492 1.000394381
## [31,] 0.44634205 1.535223223
## [32,] 0.65175149 0.950133556
## [33,] 0.83122592 0.712003997
## [34,] 0.83901689 0.709685482
## [35,] 0.35144226 1.261103502
## [36,] 0.97423126 0.486399131
## [37,] 1.41654411 1.139195360
## [38,] 2.63183509 0.822422225
## [39,] 0.63045381 1.048812011
## [40,] 0.57146521 0.235203844
## [41,] 0.58255103 1.075553654
## [42,] 0.83195385 3.509805522
## [43,] 0.43876560 0.674938357
## [44,] 3.03205793 1.528627198
## [45,] 0.73541826 0.548975511
## [46,] 0.81734820 1.135478497
## [47,] 0.66520515 0.939356479
## [48,] 2.22233465 0.835377447
## [49,] 1.03708524 1.225118334
## [50,] 0.24051637 0.002562186
## [51,] 0.44912974 1.786818579
## [52,] 1.09840206 0.777684139
## [53,] 0.12242519 1.136480694
## [54,] 0.94624480 0.281972197
## [55,] 0.85764049 1.098086792
## [56,] 0.92945815 0.727322182
## [57,] 1.12233361 1.710313997
## [58,] 0.34049949 2.396354200
## [59,] 0.21264498 1.014498396
## [60,] 0.42134724 0.901190137
## [61,] 1.07287911 0.754105954
## [62,] 1.55224313 2.083504836
## [63,] 0.54034848 0.520172598
## [64,] 0.58094094 1.124299095
## [65,] 0.44621573 1.405196331
## [66,] 0.75839148 1.455369432
## [67,] 0.69548753 1.645188107
## [68,] 0.67580723 1.139070681
## [69,] 1.02986205 0.563966151
## [70,] 0.73676292 0.680581554
## [71,] 0.30295915 0.208467470
## [72,] 0.75661983 0.517700811
## [73,] 1.22171119 1.806591956
## [74,] 1.22569404 1.518518351
## [75,] 1.14363340 0.983341434
## [76,] 0.55592047 1.575122426
## [77,] 1.06294668 0.558330705
## [78,] 1.37037649 0.320916716
## [79,] 1.89467743 0.872142426
## [80,] 0.67500443 1.761222143
## [81,] 1.28675605 2.417388870
## [82,] 1.54805396 0.698164543
## [83,] 1.45547508 2.028182509
## [84,] 0.58763708 0.599658980
## [85,] 0.92807687 1.709026930
## [86,] 0.64005088 0.740102236
## [87,] 0.54578394 0.801595372
## [88,] 1.79599479 1.500531928
## [89,] 0.90133443 0.703227840
## [90,] 0.69936616 2.191048073
## [91,] 0.93875726 0.596185445
## [92,] 0.75607402 0.525345798
## [93,] 0.34584872 0.741909916
## [94,] 0.76470944 1.565698144
## [95,] 0.63404972 0.454078514
## [96,] 0.50472178 0.310272503
## [97,] 1.16018927 1.366538857
## [98,] 0.08559623 0.654547986
## [99,] 0.62574935 1.697578182
## [100,] 1.53250519 0.530083411
## [101,] 0.77964609 1.022860016
## [102,] 0.16220950 1.118423459
## [103,] 0.22724450 1.183286670
## [104,] 0.97820090 2.323884974
## [105,] 1.27440256 0.898004413
## [106,] 0.52178300 2.160333342
## [107,] 0.60915301 0.634713157
## [108,] 0.97531782 0.774424849
## [109,] 1.02979915 1.996457404
## [110,] 1.21128766 0.661318725
## [111,] 1.13742991 0.794475584
## [112,] 1.49480314 1.257785092
## [113,] 0.30630329 1.819060124
## [114,] 1.04313623 0.190612673
## [115,] 1.12720208 0.743063741
## [116,] 1.71456332 0.764395268
## [117,] 0.82433685 0.423892531
## [118,] 0.58157710 1.630981442
## [119,] 0.50645232 0.588688186
## [120,] 0.87989761 2.113158815
## [121,] 0.74103795 1.121913244
## [122,] 0.20549892 0.790080489
## [123,] 2.71337090 0.404841827
## [124,] 2.08278409 0.653325661
## [125,] 0.93174442 0.681066462
## [126,] 0.53060081 0.640613406
## [127,] 0.64917668 0.479552931
## [128,] 0.49404401 1.190187002
## [129,] 0.21844911 0.413878150
## [130,] 0.81111435 0.580928697
## [131,] 2.47704413 0.527467157
## [132,] 1.24331927 1.071854038
## [133,] 0.42672390 0.456200346
## [134,] 0.41211975 0.337075645
## [135,] 0.77147751 1.754845752
## [136,] 2.80238185 0.980626951
## [137,] 0.70120935 0.489127611
## [138,] 0.51760489 0.397512047
## [139,] 1.33572607 0.775856272
## [140,] 0.86623847 0.792813953
## [141,] 0.21844221 1.167942045
## [142,] 0.54186621 1.790142643
## [143,] 0.61618076 0.262524246
## [144,] 0.47844914 0.631887512
## [145,] 1.50828340 0.959079815
## [146,] 1.98948838 0.598728907
## [147,] 0.73863087 1.181904896
## [148,] 0.96532303 0.590855456
## [149,] 0.67078956 0.542555851
## [150,] 0.51252224 0.585690971
## [151,] 1.53080097 0.618156610
## [152,] 1.02904577 0.978899670
## [153,] 0.67012349 0.991294697
## [154,] 0.90908833 0.451544514
## [155,] 0.28180438 1.288455029
## [156,] 2.61339834 0.597262362
## [157,] 0.27143583 1.082892473
## [158,] 0.89221906 0.229567738
## [159,] 0.62089282 1.734210019
## [160,] 0.84474508 0.761334031
## [161,] 2.91762355 0.325307744
## [162,] 1.03787067 2.799887781
## [163,] 2.40749044 0.734041427
## [164,] 1.00450343 1.353985666
## [165,] 1.58608192 1.623462864
## [166,] 0.38291788 2.485125543
## [167,] 1.85305223 1.308823956
## [168,] 0.58005503 1.262691666
## [169,] 0.20021482 1.984416492
## [170,] 0.97660894 0.781709905
## [171,] 3.37901418 1.112114707
## [172,] 1.68910183 0.650252489
## [173,] 0.77721191 0.884328887
## [174,] 1.02872283 0.698492493
## [175,] 0.98789064 0.296907917
## [176,] 0.48418149 1.114408489
## [177,] 0.45361011 0.655883174
## [178,] 1.75633045 1.132250677
## [179,] 1.10427015 1.118347770
## [180,] 1.01999422 1.859835107
## [181,] 0.75777989 0.894708631
## [182,] 0.97042288 1.103463852
## [183,] 1.75662757 0.349112567
## [184,] 0.67588475 0.168502740
## [185,] 0.97684174 0.812856488
## [186,] 1.45523223 1.538975860
## [187,] 0.54463059 0.891326940
## [188,] 0.58353773 0.624505095
## [189,] 0.38845770 1.076507368
## [190,] 0.57444286 1.665662335
## [191,] 0.56686182 0.812278388
## [192,] 0.85659490 0.756985254
## [193,] 1.47935058 0.329484511
## [194,] 1.27263244 0.909294992
## [195,] 1.08360812 0.793447319
## [196,] 1.64618286 3.237726891
## [197,] 1.78992987 2.395211356
## [198,] 1.06063522 0.376182460
## [199,] 1.30894628 0.579133893
## [200,] 0.20860238 0.621766928
## [201,] 1.16333347 1.638154744
## [202,] 0.73435879 2.921689122
## [203,] 0.57943298 0.715926849
## [204,] 0.93432456 1.144944795
## [205,] 0.76370199 2.454283420
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## [648,] 1.21322259 1.707714243
## [649,] 0.54690386 1.335263087
## [650,] 2.66417673 1.462557386
## [651,] 1.30676832 0.755528372
## [652,] 0.52469436 1.945304453
## [653,] 3.86173077 1.830407538
## [654,] 1.18444809 0.435816918
## [655,] 1.82125606 2.617005566
## [656,] 0.19790057 0.523854850
## [657,] 0.70422466 0.934002010
## [658,] 1.19381733 0.696807512
## [659,] 0.46017085 1.077844559
## [660,] 0.49166720 0.535149795
## [661,] 0.42765031 0.864401187
## [662,] 3.53894419 0.265858185
## [663,] 0.98693737 0.801352247
## [664,] 1.49382127 2.041465929
## [665,] 0.61080626 0.962745067
## [666,] 2.80924718 0.114419097
## [667,] 0.38081658 0.700526462
## [668,] 2.35511039 1.590511472
## [669,] 1.24111121 0.508660361
## [670,] 0.89265876 2.114026060
## [671,] 0.36783462 0.384634402
## [672,] 0.80772536 1.553191107
## [673,] 1.68809986 1.770857074
## [674,] 0.76527208 0.664291053
## [675,] 0.65042560 0.983895433
## [676,] 2.21279937 2.256182960
## [677,] 0.45132959 1.111522643
## [678,] 1.21129480 1.491234795
## [679,] 3.86029661 0.939579205
## [680,] 0.53941227 0.538525807
## [681,] 1.14802055 1.008733964
## [682,] 0.69388371 0.773002614
## [683,] 0.74815837 0.884456863
## [684,] 1.02817493 0.406823245
## [685,] 0.92598223 0.838030747
## [686,] 0.78671274 1.055325006
## [687,] 0.76740310 0.819601236
## [688,] 1.47519178 1.306783010
## [689,] 0.28879416 0.757153374
## [690,] 0.49349823 0.918557704
## [691,] 1.55463764 0.768794823
## [692,] 1.54590316 0.995162094
## [693,] 0.46659100 0.714231968
## [694,] 0.62298735 1.168471924
## [695,] 0.79984164 0.957905993
## [696,] 0.69823892 1.566829130
## [697,] 1.47776289 0.843541260
## [698,] 1.26554627 1.051529957
## [699,] 2.29228237 1.181286276
## [700,] 0.57813712 1.102159393
## [701,] 0.92949893 1.226139196
## [702,] 1.29383506 0.667703532
## [703,] 0.82642739 0.763494542
## [704,] 1.51304866 1.042434795
## [705,] 0.15720250 0.886484947
## [706,] 1.21764429 1.109806532
## [707,] 1.07673603 0.538353400
## [708,] 0.78133022 0.701111494
## [709,] 0.73945014 1.158065790
## [710,] 0.48185453 0.797056925
## [711,] 0.80255359 2.462794004
## [712,] 0.48444344 0.506647296
## [713,] 1.45240435 1.340167974
## [714,] 0.31867188 0.933982311
## [715,] 0.57157968 0.715218068
## [716,] 0.44811540 0.654550608
## [717,] 0.88625119 0.887138772
## [718,] 0.90037483 0.703598962
## [719,] 0.43808838 1.672990188
## [720,] 0.65274042 1.591771926
## [721,] 0.51914483 0.573659051
## [722,] 0.96645381 0.886690786
## [723,] 0.82185152 1.689822869
## [724,] 1.20085730 0.993759308
## [725,] 0.67746436 0.980658805
## [726,] 0.70221970 0.675305483
## [727,] 0.56914493 1.424074742
## [728,] 0.54142720 0.287475909
## [729,] 0.34522905 0.738983544
## [730,] 0.28691538 0.823822344
## [731,] 1.10196025 0.673946309
## [732,] 1.96134238 0.744899690
## [733,] 1.42694747 0.371913136
## [734,] 1.11450657 0.931926742
## [735,] 0.64267492 1.216285455
## [736,] 0.11265274 0.658494476
## [737,] 1.62368278 1.146288287
## [738,] 1.14794272 1.318607873
## [739,] 0.70395982 1.506289178
## [740,] 0.42531375 0.327979241
## [741,] 0.51550280 0.763256612
## [742,] 0.80383533 0.725507021
## [743,] 1.17506630 0.253464135
## [744,] 2.14238048 1.605760058
## [745,] 0.68083068 0.389817186
## [746,] 1.22737174 1.070884271
## [747,] 0.92454416 0.798308093
## [748,] 0.58430427 0.530647786
## [749,] 0.84740721 1.301859486
## [750,] 0.44731215 0.601764718
## [751,] 1.58314935 0.870996911
## [752,] 0.40348286 0.581008939
## [753,] 1.03110361 1.061115687
## [754,] 1.75496135 0.750353549
## [755,] 0.65935875 1.105800871
## [756,] 1.52157572 0.650250059
## [757,] 1.01046281 1.692003061
## [758,] 0.43132005 0.898152553
## [759,] 1.40747366 1.584099949
## [760,] 0.35366270 0.483105117
## [761,] 0.82385850 0.672143885
## [762,] 0.69104532 1.086926498
## [763,] 0.91782644 0.497329476
## [764,] 0.77147637 1.378284014
## [765,] 0.86071533 0.820852883
## [766,] 0.49714206 0.532106404
## [767,] 1.16875672 1.857008308
## [768,] 0.57425543 1.183603046
## [769,] 1.35047360 1.350757411
## [770,] 1.15666508 0.151866535
## [771,] 1.90769038 0.510204848
## [772,] 1.09339788 0.772836609
## [773,] 0.58019035 0.911516816
## [774,] 0.25667812 0.518766215
## [775,] 0.79117195 0.904268388
## [776,] 0.94181564 1.359603145
## [777,] 5.52518847 0.221073796
## [778,] 0.68135300 1.696725260
## [779,] 0.92245456 1.049484243
## [780,] 1.68195247 0.930888273
## [781,] 2.12004713 0.683446144
## [782,] 0.94399667 0.798878475
## [783,] 0.80970692 0.407977775
## [784,] 0.44749460 0.762961195
## [785,] 0.25953608 0.296056352
## [786,] 0.32713801 0.733673194
## [787,] 0.54218799 1.808531355
## [788,] 2.28988886 1.332588124
## [789,] 1.31679614 1.241637145
## [790,] 0.16346977 1.178675548
## [791,] 0.24150972 1.225104840
## [792,] 0.79007722 0.759797427
## [793,] 0.22274024 0.844680771
## [794,] 0.30385244 0.233305839
## [795,] 1.49557816 0.551533783
## [796,] 0.52825890 0.920091772
## [797,] 0.68527359 0.267664319
## [798,] 0.45830871 1.292852689
## [799,] 2.90123020 2.394361313
## [800,] 0.86270874 1.882504752
## [801,] 0.80525226 0.616197208
## [802,] 0.31997388 1.170702585
## [803,] 1.72132129 1.154298317
## [804,] 1.16439418 0.531915886
## [805,] 0.84010486 1.518254169
## [806,] 0.96511560 0.944098878
## [807,] 0.75463932 1.570759284
## [808,] 1.25787354 0.308987239
## [809,] 2.10763560 1.676883188
## [810,] 1.25655583 0.771741523
## [811,] 1.20149126 0.731191059
## [812,] 0.53070646 0.769668920
## [813,] 1.69905111 0.198984027
## [814,] 2.52031926 1.324276646
## [815,] 0.40835015 1.135312240
## [816,] 0.93073208 0.546698241
## [817,] 0.91087932 1.500447019
## [818,] 0.70441165 0.263608647
## [819,] 0.32905113 1.061831700
## [820,] 0.66708075 0.382963821
## [821,] 0.14945886 1.093420688
## [822,] 2.75666456 0.158634585
## [823,] 1.33993755 0.666622728
## [824,] 1.17638970 0.708128558
## [825,] 3.77553842 0.576286528
## [826,] 0.89523937 0.542694884
## [827,] 0.88153496 0.583358632
## [828,] 1.00463222 0.970043484
## [829,] 1.60291820 0.619380405
## [830,] 0.29489239 0.975114041
## [831,] 0.44772027 0.871559185
## [832,] 0.59237202 0.724704578
## [833,] 0.33830827 0.974996922
## [834,] 0.57771522 0.894641996
## [835,] 0.89862663 1.098393444
## [836,] 1.49367706 0.116923718
## [837,] 0.85707026 0.443766232
## [838,] 1.66491824 1.303914743
## [839,] 1.34132231 0.525003449
## [840,] 0.94420586 2.379559590
## [841,] 0.75064053 0.653922143
## [842,] 0.75820232 1.091435802
## [843,] 1.03625506 1.179135151
## [844,] 0.67274508 1.681974599
## [845,] 0.90813546 0.842629672
## [846,] 1.01806207 1.448815858
## [847,] 1.73529609 0.371176157
## [848,] 1.26222111 1.488543811
## [849,] 0.51716733 0.325703798
## [850,] 0.97359801 0.704520483
## [851,] 3.96758654 1.560948963
## [852,] 0.41034869 0.796307923
## [853,] 0.90399150 1.137231132
## [854,] 1.14782493 1.510729383
## [855,] 0.44503022 1.060149094
## [856,] 0.47796841 0.556517255
## [857,] 1.48282860 0.793160690
## [858,] 1.36549478 0.865712171
## [859,] 2.67102031 2.158056387
## [860,] 0.70719133 0.243061677
## [861,] 1.72482059 0.718642013
## [862,] 0.36235592 0.453840089
## [863,] 2.56210066 2.240706731
## [864,] 0.55995507 0.998508563
## [865,] 0.87893592 0.647022620
## [866,] 0.90718386 0.295301729
## [867,] 1.07992040 1.544006792
## [868,] 0.59211841 0.800328122
## [869,] 0.72841607 2.030364131
## [870,] 2.05777125 0.757639295
## [871,] 2.87808060 0.835113742
## [872,] 1.26749428 0.566076485
## [873,] 2.27317011 0.740802373
## [874,] 1.29566331 1.040330032
## [875,] 0.27534301 1.145961875
## [876,] 1.21494590 0.620007632
## [877,] 0.50013953 0.624180629
## [878,] 0.93841010 1.225163545
## [879,] 1.63438573 1.704152125
## [880,] 1.80754685 1.427108281
## [881,] 2.49556679 3.477827991
## [882,] 1.25211336 0.131881464
## [883,] 2.26317658 0.717512260
## [884,] 0.42166616 0.646584241
## [885,] 0.79539923 0.479127674
## [886,] 1.66290424 0.448484401
## [887,] 0.92286110 0.976964434
## [888,] 0.20194136 0.623817267
## [889,] 0.89278204 1.318955318
## [890,] 0.78444176 0.289347397
## [891,] 0.65463272 1.427884782
## [892,] 1.41358014 3.052740899
## [893,] 0.92392622 1.536152620
## [894,] 0.17260471 0.961852235
## [895,] 0.46317931 1.767607852
## [896,] 0.27247005 0.913904269
## [897,] 0.56690424 0.455747271
## [898,] 0.44148609 1.221163421
## [899,] 1.37089255 1.176935831
## [900,] 0.76873569 1.238497297
## [901,] 1.69307488 3.273560778
## [902,] 0.59952479 0.248370190
## [903,] 1.26545107 0.231434942
## [904,] 0.89573753 0.820040162
## [905,] 0.82149866 0.830034568
## [906,] 0.29146458 0.994442107
## [907,] 0.92161741 0.540857683
## [908,] 0.82657083 1.130221193
## [909,] 1.07260707 1.004319383
## [910,] 0.93270484 2.844378759
## [911,] 2.14962376 0.879296573
## [912,] 1.31770177 0.414843430
## [913,] 0.69593518 1.420558814
## [914,] 0.69282340 1.951589954
## [915,] 1.55937784 0.359919899
## [916,] 2.55177865 0.608914782
## [917,] 0.83913402 1.190552600
## [918,] 0.80033774 0.806860962
## [919,] 1.82864872 1.275365856
## [920,] 1.08811604 0.579925761
## [921,] 0.98781685 0.893113803
## [922,] 0.54238893 1.612650122
## [923,] 1.05293723 0.595826634
## [924,] 0.83471490 1.491954192
## [925,] 0.47256689 1.078259600
## [926,] 1.49657778 0.718233202
## [927,] 0.80602665 0.740200203
## [928,] 0.29522740 0.694396450
## [929,] 0.40266870 0.315911390
## [930,] 1.09114637 0.858224900
## [931,] 0.50825096 1.089501831
## [932,] 0.16597464 1.299739114
## [933,] 0.69880328 1.282196119
## [934,] 0.89780478 1.344691195
## [935,] 0.75788250 1.150674192
## [936,] 0.94001244 0.292282395
## [937,] 1.16333149 0.980252516
## [938,] 1.84294772 0.824957238
## [939,] 0.26372511 1.033409427
## [940,] 1.01112672 0.532863059
## [941,] 1.49115404 1.154852125
## [942,] 0.57814286 0.729939942
## [943,] 0.68738150 0.912512192
## [944,] 0.63402394 0.592203205
## [945,] 0.59287468 0.942758912
## [946,] 0.20319084 0.332591393
## [947,] 0.36511823 0.232451157
## [948,] 0.36432500 0.290728993
## [949,] 1.00328199 1.223967961
## [950,] 0.83878346 0.574566600
## [951,] 0.80452490 1.233056641
## [952,] 0.43636043 0.452254133
## [953,] 2.95946160 1.713495305
## [954,] 0.70927671 0.914937445
## [955,] 0.87313051 1.583903839
## [956,] 0.27211123 0.397220475
## [957,] 0.59841670 0.436699228
## [958,] 2.22848411 0.592353639
## [959,] 0.80391666 1.719901680
## [960,] 0.82339076 1.511086453
## [961,] 0.56146826 0.204092097
## [962,] 0.57905695 0.624537542
## [963,] 0.91953409 0.767203783
## [964,] 1.12158030 3.794498035
## [965,] 0.47249768 1.359916098
## [966,] 0.41982676 2.446763641
## [967,] 1.33098810 1.784839617
## [968,] 0.80076476 0.859812457
## [969,] 0.80434215 0.901188487
## [970,] 0.50037988 0.749066362
## [971,] 0.65788204 1.205801096
## [972,] 1.17092973 1.401353931
## [973,] 0.74525667 1.289981407
## [974,] 1.10183139 0.679747295
## [975,] 0.52289264 1.001014066
## [976,] 0.33580336 0.481234523
## [977,] 1.78442880 2.798897738
## [978,] 0.51141648 1.097367186
## [979,] 0.34620161 0.846713273
## [980,] 0.33394149 0.225208232
## [981,] 0.70380781 1.117636144
## [982,] 1.01154058 3.097295097
## [983,] 3.31542666 1.521215716
## [984,] 0.41733718 0.433503284
## [985,] 0.48217266 1.288759019
## [986,] 0.89866837 0.714814256
## [987,] 0.72729241 0.529683915
## [988,] 1.18292344 2.953227145
## [989,] 1.53020639 0.332466765
## [990,] 0.94141883 0.892244690
## [991,] 2.51454734 3.686478225
## [992,] 1.59313983 0.856957474
## [993,] 0.72755285 1.418979712
## [994,] 0.44822243 0.797848194
## [995,] 0.91740605 0.421418312
## [996,] 0.70963245 1.053679065
## [997,] 0.61577238 1.991289185
## [998,] 0.23909102 1.782428051
## [999,] 0.75763838 0.616216765
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
##
## $terms
## pca_data ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist <- vegdist(pca_data, species = "bray")
res <- pcoa(dist)
p1 <- as.data.frame(res$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env,.)
ggplot(p1, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
m02 <- betadisper(dist, env$microsite)
m02
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5063 0.4721
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m02)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 0.7313
## Residuals 20 1.05203 0.052602
permutest(m02,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 99 0.74
## Residuals 20 1.05203 0.052602
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.72
## Open 0.73133
m02.HSD <- TukeyHSD(m02)
boxplot(m02)
m03 <- betadisper(dist, env$shrub_density)
m03
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4721 0.5171 0.4968 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m03)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 0.08827 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(m03,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 99 0.09 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.76000 0.80000
## 10 0.76968 0.89000
## 11 0.84659 0.89212
## 12
## 13
## 14
m03.HSD <- TukeyHSD(m03)
boxplot(m03)
m04 <- betadisper(dist, env$site)
m04
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2828 0.3427 0.4625
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m04)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 0.3247
## Residuals 19 0.89240 0.046969
permutest(m04,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 99 0.37
## Residuals 19 0.89240 0.046969
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.61000 0.20
## Cuyama 0.54695 0.32
## Tecopa 0.19772 0.31784
m04.HSD <- TukeyHSD(m04)
boxplot(m04)
### 2023 Data
photo_2023 <- read.csv("observations_2023.csv")
summary(photo_2023)
## region site site_code microsite
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.00 Length:192701 Length:192701
## 1st Qu.:1.000 1st Qu.:1.00 Class :character Class :character
## Median :2.000 Median :1.00 Mode :character Mode :character
## Mean :1.771 Mean :1.49
## 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :4.000 Max. :2.00
## year shrub_density rep identified_by
## Min. :2023 Min. : 0.000 Min. : 1 Length:192701
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 48176 Class :character
## Median :2023 Median :10.000 Median : 96351 Mode :character
## Mean :2023 Mean : 6.239 Mean : 96351
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:144526
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:192701 Length:192701 Min. :0.000000 Length:192701
## Class :character Class :character 1st Qu.:0.000000 Class :character
## Mode :character Mode :character Median :0.000000 Mode :character
## Mean :0.005392
## 3rd Qu.:0.000000
## Max. :1.000000
## order family genus species
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:192701 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :2
photo_2023 <- photo_2023 %>%
filter(common_name != "Human")
photo_2023 <- photo_2023 %>%
filter(common_name != "Human-Camera Trapper")
photo_2023 <- photo_2023 %>%
filter(common_name != "Domestic Dog")
photo_2023 <- photo_2023 %>%
filter(common_name != "Vehicle")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Insect")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Animal")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Bird")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "No CV Result")
count.hit_2023 <- photo_2023 %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_2023)
## animal.hit n
## Min. :0.00 Min. : 546
## 1st Qu.:0.25 1st Qu.: 48325
## Median :0.50 Median : 96104
## Mean :0.50 Mean : 96104
## 3rd Qu.:0.75 3rd Qu.:143883
## Max. :1.00 Max. :191662
### 2023 had a 0.28% capture rate
### Animal Observations by Site_Code
animals_by_sitecode_2023 <- photo_2023%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_2023 <- animals_by_sitecode_2023 %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Animal observations by Site 2023
animals_by_site_2023 <- photo_2023 %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_2023 <- animals_by_site_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Animal observations by Density
animals_by_density_2023 <- photo_2023 %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_2023 <- animals_by_density_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Total Observations 2023
Total_Observations_2023 <- photo_2023 %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
density_obvs_2023 <- merge(animals_by_density_2023, Total_Observations_2023, all = TRUE)
density_obvs_2023$percent_presence <- density_obvs_2023$captures/density_obvs_2023$total
### Percent proportion Figure
plot2 <- ggplot(density_obvs_2023, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot2 + scale_fill_manual(values = c("#009900", "#0066cc"))
m2<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_2023)
anova(m2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 37 2413.4
## microsite 1 89.8 36 2323.6 <2e-16 ***
## common_name 24 2323.6 12 0.0 <2e-16 ***
## microsite:common_name 12 0.0 0 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(m2, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
#head(e2)
animals_density_2023 <- photo_2023 %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_2023 <- animals_density_2023 %>% filter(common_name != "Blank")
pca_data_2023 <- animals_density_2023 ### Created new df for pcoa data
pca_data_2023 <- pca_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_2023)
## [1] 23 26
env_2023 <- read.csv("environment_2023.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22 5
model01 <- adonis(pca_data_2023 ~ microsite*shrub_density, data = env_2023)
## 'adonis' will be deprecated: use 'adonis2' instead
model01
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.4511 0.45114 1.6082 0.06930 0.129
## shrub_density 1 0.4482 0.44823 1.5978 0.06885 0.124
## Residuals 20 5.6105 0.28052 0.86184
## Total 22 6.5099 1.00000
##
## $call
## adonis(formula = pca_data_2023 ~ microsite * shrub_density, data = env_2023)
##
## $coefficients
## shrub_density Black-tailed Jackrabbit
## (Intercept) -10.398601 2.8438228
## microsite1 -14.216783 -3.7925408
## shrub_density 2.876923 0.4769231
## microsite1:shrub_density NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.545455e-02 0.12820513
## microsite1 -4.545455e-02 0.12820513
## shrub_density -6.331379e-18 -0.01538462
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) -2.5571096 -0.21794872
## microsite1 -2.6480186 -0.21794872
## shrub_density 0.4923077 0.04615385
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -1.8205128 0.47435897 2.6340326
## microsite1 -1.8205128 0.47435897 1.7249417
## shrub_density 0.3384615 -0.07692308 -0.2615385
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.060606 -2.3391608 4.545455e-02
## microsite1 1.606061 -2.4300699 -4.545455e-02
## shrub_density -0.200000 0.4461538 8.356568e-18
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Heermann's Kangaroo Rat Horned Lark
## (Intercept) 0.03263403 -7.849650 0.38461538
## microsite1 -0.05827506 -15.304196 0.38461538
## shrub_density 0.06153846 3.169231 -0.04615385
## microsite1:shrub_density NA NA NA
## Kit Fox Lizards and Snakes Loggerhead Shrike
## (Intercept) 2.1165501 2.727273e-01 -0.4801865
## microsite1 1.9347319 -2.727273e-01 -0.5710956
## shrub_density -0.3230769 -1.513076e-17 0.1230769
## microsite1:shrub_density NA NA NA
## Mammal Merriam's Kangaroo Rat
## (Intercept) -0.21794872 1.10256410
## microsite1 -0.21794872 0.10256410
## shrub_density 0.04615385 -0.09230769
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel Salinas Pocket Mouse
## (Intercept) 0.3694639 -0.39044289
## microsite1 -0.2668998 -0.48135198
## shrub_density 0.1538462 0.09230769
## microsite1:shrub_density NA NA
## Say's Phoebe Vesper Sparrow Western whiptail
## (Intercept) 0.264568765 1.5384615 0.12820513
## microsite1 -0.008158508 1.5384615 0.12820513
## shrub_density -0.015384615 -0.1846154 -0.01538462
## microsite1:shrub_density NA NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.35547786
## microsite1 -0.09906760
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 1.08713373 1.19242360 1.04021622 0.53368864
## microsite1 0.27702067 0.36812565 0.28650205 -0.08921598
## shrub_density -0.05750503 -0.07418322 -0.03323392 0.02592824
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 1.09169046 0.725232927 0.9149049 0.73036046
## microsite1 0.43517627 -0.003397368 0.1285966 -0.03557956
## shrub_density -0.08696283 -0.012927632 -0.0247195 0.01666357
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.3003752 1.4998669 0.80294716 0.64129703
## microsite1 0.6190745 0.7936762 0.05159731 -0.03324735
## shrub_density -0.1227435 -0.1491194 -0.01154984 0.01898538
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.99239018 0.66809163 0.63858697 -0.08681298
## microsite1 0.34039631 -0.04876636 -0.21003546 -0.87304773
## shrub_density -0.06950611 -0.01035118 0.04254088 0.14042524
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.17811261 0.1694050 -0.01538676 0.91285079
## microsite1 -0.64882951 -0.6891926 -0.81989153 0.22319732
## shrub_density 0.09926822 0.1072032 0.13358216 -0.04137692
## microsite1:shrub_density NA NA NA NA
## 21 22 23
## (Intercept) 0.95345668 1.13091299 0.9134932
## microsite1 0.30456994 0.48161788 0.2756840
## shrub_density -0.05633216 -0.08893335 -0.0522899
## microsite1:shrub_density NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 2.7382748 0.7171803
## [2,] 1.0996783 1.4344806
## [3,] 2.3782813 0.8155126
## [4,] 0.8280660 0.4049624
## [5,] 0.7374631 1.0327982
## [6,] 0.5944951 0.4515525
## [7,] 0.2700776 1.8120483
## [8,] 1.6040864 1.2528508
## [9,] 0.3660925 2.0217544
## [10,] 1.0117398 1.7981395
## [11,] 0.9781887 2.1096450
## [12,] 0.3537379 0.3642225
## [13,] 0.6869765 0.6567368
## [14,] 0.3115720 2.1928635
## [15,] 1.7331453 1.0169350
## [16,] 0.9596079 0.9861569
## [17,] 1.8406550 0.4119059
## [18,] 0.3954707 1.0367186
## [19,] 0.4924088 1.2389746
## [20,] 1.0030954 0.6866150
## [21,] 1.0640408 0.6412791
## [22,] 0.9843796 0.3781761
## [23,] 0.7182460 0.9079104
## [24,] 0.6055968 0.8098724
## [25,] 0.2824401 0.6837748
## [26,] 0.5465991 1.1281813
## [27,] 0.9923677 0.9763268
## [28,] 1.6439075 2.2489744
## [29,] 1.5983089 1.0626882
## [30,] 0.3395985 0.2210830
## [31,] 0.5354982 1.0033802
## [32,] 0.4518611 1.0375600
## [33,] 0.9156338 0.6945022
## [34,] 0.7326711 0.8719917
## [35,] 1.3270950 1.1630793
## [36,] 0.8555655 0.5909281
## [37,] 1.1364610 1.9181392
## [38,] 0.9847799 1.1672615
## [39,] 0.6691929 1.2085231
## [40,] 1.0459579 1.4759968
## [41,] 1.0358562 0.8281517
## [42,] 0.1351022 0.7852149
## [43,] 2.1573028 1.0567682
## [44,] 1.2066674 0.5195105
## [45,] 0.7119275 1.1815594
## [46,] 1.3999410 0.8208377
## [47,] 0.8715805 0.4762804
## [48,] 1.0036517 0.3713635
## [49,] 0.9013351 1.1669415
## [50,] 0.7913610 1.3989553
## [51,] 0.8824895 1.3064191
## [52,] 1.5838122 0.3706992
## [53,] 0.4668202 0.5556006
## [54,] 1.3747239 0.7677864
## [55,] 0.7102356 0.7489866
## [56,] 1.1258842 0.9371586
## [57,] 0.7633517 1.4325719
## [58,] 1.0539526 0.8244041
## [59,] 0.7733155 0.5959915
## [60,] 2.1202396 1.2558250
## [61,] 0.4000565 0.6605348
## [62,] 0.7346400 0.7047121
## [63,] 0.5636768 0.4251710
## [64,] 1.3602660 0.9158437
## [65,] 3.5742708 1.1446878
## [66,] 0.5392069 1.0717975
## [67,] 1.0402897 2.7969678
## [68,] 0.8564135 0.9154163
## [69,] 1.3147426 1.0918524
## [70,] 1.0151126 0.8037166
## [71,] 2.6822804 1.0277958
## [72,] 0.8316963 1.2658315
## [73,] 3.1110483 1.5605846
## [74,] 0.7861471 0.6792935
## [75,] 0.8190844 0.2677384
## [76,] 1.4115869 0.8721571
## [77,] 0.4187621 1.5049565
## [78,] 1.3344876 1.4396763
## [79,] 0.8082365 0.3968630
## [80,] 1.6092280 2.4011541
## [81,] 0.6209134 0.7288840
## [82,] 1.5338524 0.7781797
## [83,] 0.4463297 1.3301870
## [84,] 0.5014001 1.6453936
## [85,] 2.1915007 0.4617612
## [86,] 0.1755958 1.2038608
## [87,] 0.9807270 1.7546097
## [88,] 1.0925057 0.5535764
## [89,] 1.1285489 0.9410264
## [90,] 1.3845553 1.0935328
## [91,] 0.8005270 0.8543149
## [92,] 1.7747441 0.8148711
## [93,] 0.9656347 0.8850079
## [94,] 0.6724260 0.9625644
## [95,] 0.2211525 0.8763503
## [96,] 0.9643899 0.9209384
## [97,] 0.2984458 0.5916444
## [98,] 0.8318393 1.0643771
## [99,] 0.3485737 0.5600867
## [100,] 1.4211614 0.5178163
## [101,] 0.8369026 1.5334397
## [102,] 0.9811977 1.2179178
## [103,] 1.6022900 0.7091963
## [104,] 0.5926428 0.4787760
## [105,] 0.7038819 1.8297473
## [106,] 0.6762639 0.1467004
## [107,] 0.9063436 0.6218488
## [108,] 0.7048776 1.2205993
## [109,] 1.0545086 0.3796934
## [110,] 0.3046786 0.5413877
## [111,] 1.0339587 0.7049948
## [112,] 0.6323795 1.2105481
## [113,] 0.8595198 0.6784251
## [114,] 0.4045428 0.4594704
## [115,] 0.7212456 1.2271784
## [116,] 0.5415000 0.7366891
## [117,] 0.7969268 0.8390085
## [118,] 0.7309664 0.7818723
## [119,] 1.1482505 1.4215844
## [120,] 1.3587255 1.5311380
## [121,] 1.3841010 1.5219691
## [122,] 0.3496842 1.5392874
## [123,] 0.9228000 0.3786625
## [124,] 0.7945953 1.9580517
## [125,] 0.8799227 1.0240600
## [126,] 1.5267668 0.5968688
## [127,] 0.6450349 1.5959289
## [128,] 1.3204904 2.2284481
## [129,] 0.4517017 0.2286479
## [130,] 1.7248174 2.2161323
## [131,] 0.5792634 0.4573465
## [132,] 0.2539590 0.4914602
## [133,] 0.9787231 1.3675160
## [134,] 2.0534593 1.8354910
## [135,] 0.8936060 1.2910296
## [136,] 1.1117847 1.1632545
## [137,] 0.7230107 0.6297134
## [138,] 0.4846413 0.7945592
## [139,] 0.5727237 0.5243089
## [140,] 0.3890955 0.5364520
## [141,] 1.5884495 0.4146156
## [142,] 1.6719877 1.2796764
## [143,] 0.7227712 1.4025224
## [144,] 1.6980909 1.0824444
## [145,] 0.8471497 0.7650705
## [146,] 0.7701617 0.7934832
## [147,] 0.4243351 0.8323766
## [148,] 1.0581203 2.0158779
## [149,] 1.4292932 1.0927163
## [150,] 1.5770383 0.7831433
## [151,] 0.4843029 2.5750417
## [152,] 2.3321275 1.4589910
## [153,] 0.9541153 1.4787789
## [154,] 0.8462835 1.0951974
## [155,] 0.4845349 0.5057734
## [156,] 0.4447651 0.5497398
## [157,] 1.4249147 1.4249616
## [158,] 0.8641810 0.5353499
## [159,] 0.7535915 1.4157121
## [160,] 0.7211604 1.7423315
## [161,] 1.6697859 1.0926917
## [162,] 0.4850397 1.0495755
## [163,] 0.9494819 1.0830195
## [164,] 0.5013202 0.3453050
## [165,] 1.4455322 1.4107392
## [166,] 0.4575864 1.0590453
## [167,] 0.8587519 0.9189470
## [168,] 1.7314059 0.5817640
## [169,] 1.1446649 1.2892957
## [170,] 0.1944590 0.4050381
## [171,] 1.0248612 0.9545152
## [172,] 1.7270129 1.7498332
## [173,] 0.6096415 0.2546216
## [174,] 0.8369686 0.6958042
## [175,] 0.4132246 0.8197026
## [176,] 0.3661891 1.0796383
## [177,] 0.6077763 1.0529853
## [178,] 1.0715529 1.0561281
## [179,] 1.7001175 0.7091150
## [180,] 0.7361720 0.8475822
## [181,] 0.8762563 1.0379011
## [182,] 1.2684685 1.1919251
## [183,] 0.4276396 1.2611834
## [184,] 2.8355078 0.9993001
## [185,] 0.7768380 1.2091902
## [186,] 0.2092950 1.0856551
## [187,] 0.8358542 1.8473285
## [188,] 0.4156293 1.2109307
## [189,] 0.3714976 0.7367634
## [190,] 1.4092837 0.8752986
## [191,] 2.8103818 1.5931489
## [192,] 0.4381570 0.9683500
## [193,] 0.6628551 1.4338904
## [194,] 1.3559238 0.5258778
## [195,] 1.0831014 0.4337764
## [196,] 0.7431218 1.5546510
## [197,] 0.7119704 0.7677798
## [198,] 0.6519377 1.2742883
## [199,] 1.6637430 1.7226449
## [200,] 0.7407142 1.9124531
## [201,] 2.3250093 0.7669119
## [202,] 0.6430732 1.4264210
## [203,] 0.2251508 0.7628526
## [204,] 0.7382890 1.7471426
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## [680,] 0.6412628 0.8109772
## [681,] 0.9628359 1.9724261
## [682,] 0.8670111 1.6454024
## [683,] 1.5338059 0.9964522
## [684,] 2.9887641 2.7427659
## [685,] 0.7956538 0.7227134
## [686,] 0.7892485 0.7129698
## [687,] 1.3580228 0.3872722
## [688,] 0.4558355 0.2064994
## [689,] 0.3427388 0.6539610
## [690,] 1.2523257 0.3179014
## [691,] 1.6368459 0.5922142
## [692,] 0.9199678 1.3040585
## [693,] 1.9356236 0.4282529
## [694,] 0.6056766 0.2658254
## [695,] 1.2626442 0.8848786
## [696,] 0.7682327 0.5175373
## [697,] 0.5825086 0.8098549
## [698,] 1.5933354 0.7877992
## [699,] 0.2719387 0.6760623
## [700,] 0.4128934 1.2284595
## [701,] 1.1414389 0.5265969
## [702,] 0.7433817 0.9231503
## [703,] 1.0971801 0.6835050
## [704,] 0.4767049 0.5936488
## [705,] 0.7569361 0.8945945
## [706,] 0.4282155 1.1404023
## [707,] 1.1455097 0.5262836
## [708,] 0.5582155 1.4052397
## [709,] 0.6680880 1.0939124
## [710,] 0.8419851 1.0648067
## [711,] 0.8756049 0.6588284
## [712,] 1.4855438 0.7858286
## [713,] 0.7952430 0.6860099
## [714,] 0.5025330 0.6946269
## [715,] 1.2321457 0.4262903
## [716,] 0.2619687 1.5484430
## [717,] 1.2201731 1.0484016
## [718,] 2.1205141 0.8132115
## [719,] 0.8514425 0.4393004
## [720,] 1.3000004 1.0212809
## [721,] 0.1225122 0.3543487
## [722,] 0.9681674 1.3429191
## [723,] 0.2463508 0.9524104
## [724,] 0.8208026 0.3447471
## [725,] 0.9683591 0.8327358
## [726,] 0.6633877 1.0330029
## [727,] 0.5692377 0.1299180
## [728,] 0.3754544 0.6661033
## [729,] 2.3392762 0.2893635
## [730,] 1.8306658 1.5224859
## [731,] 1.1100527 2.6259266
## [732,] 0.1557883 1.0154624
## [733,] 3.7232258 0.5923218
## [734,] 3.0063114 0.9045910
## [735,] 1.0610028 1.2432407
## [736,] 0.3983563 0.8284331
## [737,] 0.8586571 0.2284890
## [738,] 0.9638408 0.9854984
## [739,] 1.3245368 0.5772708
## [740,] 2.3741223 0.9075456
## [741,] 0.3098802 0.5827357
## [742,] 0.3078606 0.8773913
## [743,] 1.0113884 1.4884377
## [744,] 1.1828203 1.7302464
## [745,] 1.4581638 1.1030047
## [746,] 0.8684319 1.1516594
## [747,] 1.0032254 0.6669102
## [748,] 0.9772989 1.1749810
## [749,] 0.6738043 0.4676554
## [750,] 0.1787842 0.5501696
## [751,] 1.0044021 0.4933203
## [752,] 0.6656481 0.6216719
## [753,] 0.4623568 1.1749961
## [754,] 0.8352325 0.7693856
## [755,] 0.4201885 1.4117215
## [756,] 1.0449122 0.7835850
## [757,] 0.6253478 0.9113521
## [758,] 0.8110692 1.9625250
## [759,] 0.2881151 0.9992459
## [760,] 0.7227180 0.8364296
## [761,] 0.2515428 1.5086606
## [762,] 0.8005548 1.0997582
## [763,] 0.2021728 0.9759520
## [764,] 0.8803604 0.7169446
## [765,] 0.6033294 1.0744713
## [766,] 0.5695719 0.5380103
## [767,] 2.1844210 1.2882888
## [768,] 1.3109725 0.8343514
## [769,] 0.7734289 1.1705051
## [770,] 1.2772370 1.7982242
## [771,] 0.8510570 1.4341436
## [772,] 0.2730946 1.4520035
## [773,] 0.5088346 2.1815348
## [774,] 0.8962447 1.0425626
## [775,] 1.1506337 0.8851086
## [776,] 1.0556097 0.7612639
## [777,] 1.3099935 1.9880015
## [778,] 0.6541766 1.3677700
## [779,] 1.0367594 0.7206970
## [780,] 1.1401624 0.5296758
## [781,] 1.3727543 2.6433162
## [782,] 0.7209186 1.2824811
## [783,] 1.2370039 0.5652595
## [784,] 1.3016810 1.0655017
## [785,] 1.0651528 0.9900332
## [786,] 0.9192623 1.0846283
## [787,] 2.7316337 0.8888452
## [788,] 1.2356377 0.6072537
## [789,] 0.8429879 1.7774124
## [790,] 0.6792695 0.7237951
## [791,] 0.5137883 1.0117059
## [792,] 0.6975670 0.5835361
## [793,] 0.6959790 0.3623321
## [794,] 0.9974067 0.7849763
## [795,] 1.0444049 0.9014131
## [796,] 0.3951821 1.6046673
## [797,] 0.4800807 0.7236967
## [798,] 0.6743464 1.7826001
## [799,] 1.6446054 0.4519389
## [800,] 1.5838862 1.2844503
## [801,] 1.7409729 0.5148704
## [802,] 0.4461590 0.6117800
## [803,] 0.6893886 0.4595770
## [804,] 1.2506738 1.9013684
## [805,] 2.0517455 2.0358691
## [806,] 0.6228277 0.4565032
## [807,] 0.9259661 1.2399171
## [808,] 1.9627551 1.2840617
## [809,] 0.6537201 1.0257056
## [810,] 0.6513838 0.6273337
## [811,] 0.7915049 0.3057482
## [812,] 0.3961761 1.2712514
## [813,] 0.6472418 1.5030755
## [814,] 2.2888326 1.2461410
## [815,] 0.4725135 1.4036298
## [816,] 0.5357719 0.5807133
## [817,] 1.5824029 2.2088563
## [818,] 0.6255094 1.5868429
## [819,] 0.7765597 1.0682722
## [820,] 0.5841069 0.8499209
## [821,] 0.3585005 1.0925057
## [822,] 1.1378691 0.4505460
## [823,] 1.6453440 0.5690062
## [824,] 0.3341070 1.7304638
## [825,] 0.2129236 0.8903641
## [826,] 0.7305571 0.8944324
## [827,] 0.2592391 1.8510019
## [828,] 0.7313600 1.3096716
## [829,] 1.2225318 0.6868632
## [830,] 1.0805237 1.2376928
## [831,] 0.6861552 0.7704690
## [832,] 1.0310521 1.1217127
## [833,] 0.7229002 0.6008686
## [834,] 0.8594878 0.5090439
## [835,] 0.6456431 0.5389815
## [836,] 1.0497801 0.9056697
## [837,] 0.7743263 0.5823938
## [838,] 0.2775914 0.6600846
## [839,] 1.0734097 1.0981067
## [840,] 1.3724453 0.6762862
## [841,] 0.6200563 1.4369126
## [842,] 0.4221652 0.7497118
## [843,] 0.1887104 0.4511190
## [844,] 0.8905813 1.0713214
## [845,] 0.4492508 0.9611427
## [846,] 1.2266377 0.9239308
## [847,] 1.8357903 0.6139745
## [848,] 2.1170610 1.5297623
## [849,] 0.8641904 0.9048875
## [850,] 0.7763564 0.8261014
## [851,] 0.5886437 0.9219957
## [852,] 0.7780805 1.2198779
## [853,] 0.3130145 1.0273264
## [854,] 1.3037881 1.7243142
## [855,] 0.6600778 0.1135421
## [856,] 0.3405642 1.2357915
## [857,] 1.6048183 1.0667869
## [858,] 0.7850148 0.9659300
## [859,] 1.4330423 0.7837892
## [860,] 1.9417944 1.5471746
## [861,] 0.7368109 0.9799005
## [862,] 0.4443651 1.4897043
## [863,] 0.8096961 1.5027805
## [864,] 0.3342372 0.5853036
## [865,] 0.6936222 1.0112454
## [866,] 0.7970887 1.7889110
## [867,] 0.9834582 0.7641085
## [868,] 0.7640428 0.5700905
## [869,] 1.6414408 0.5670252
## [870,] 0.9302196 1.7945324
## [871,] 0.8533056 0.4033542
## [872,] 0.2482495 1.1677066
## [873,] 0.7998040 0.8428989
## [874,] 0.8520182 0.3752923
## [875,] 0.6009293 1.3048680
## [876,] 0.3443136 0.9950623
## [877,] 2.2497036 0.6300975
## [878,] 0.7127964 1.6250996
## [879,] 0.9553326 1.3863370
## [880,] 0.3400701 1.0058723
## [881,] 0.8357676 0.4586675
## [882,] 0.6962055 0.2049235
## [883,] 1.0528344 0.9543984
## [884,] 0.6551738 1.5340469
## [885,] 1.9641078 1.6183076
## [886,] 1.0440074 0.5125522
## [887,] 0.8646239 0.5517355
## [888,] 0.6761364 2.8340474
## [889,] 0.7399453 1.2953866
## [890,] 1.4787130 1.0693455
## [891,] 1.1775275 2.5238715
## [892,] 2.1987639 0.7572682
## [893,] 1.7449447 1.4987778
## [894,] 0.2214034 1.1560529
## [895,] 1.9745993 0.4945942
## [896,] 1.5273327 0.4593395
## [897,] 1.1017223 0.4914373
## [898,] 0.6719017 1.1670288
## [899,] 0.3934572 0.5766324
## [900,] 0.3379552 2.1638225
## [901,] 0.4180118 1.4133276
## [902,] 0.5052528 0.2869777
## [903,] 1.1425264 0.5547063
## [904,] 4.2820413 1.1374841
## [905,] 0.6944172 0.5522295
## [906,] 1.0518811 0.9191484
## [907,] 1.1865574 0.9241486
## [908,] 1.4206617 0.3614167
## [909,] 0.5846067 0.6777883
## [910,] 0.8763929 1.5577614
## [911,] 0.7876491 1.1484446
## [912,] 0.5163299 0.3901655
## [913,] 0.8104888 2.4508215
## [914,] 0.7794721 0.5401396
## [915,] 1.2704495 0.3891202
## [916,] 1.9685345 1.7703449
## [917,] 0.7167361 0.4780141
## [918,] 1.0570261 0.9129683
## [919,] 0.5217457 1.0365086
## [920,] 1.6125546 1.2231105
## [921,] 0.7641024 0.6249500
## [922,] 0.5860518 0.9734472
## [923,] 0.9238994 1.2624703
## [924,] 1.8888542 1.5093125
## [925,] 2.3520741 0.5955637
## [926,] 1.1756421 2.0979918
## [927,] 2.3694816 1.2120009
## [928,] 1.2284399 0.2796685
## [929,] 0.6708727 0.7965035
## [930,] 1.3412596 0.5425654
## [931,] 0.6410777 1.1444281
## [932,] 0.9475407 0.3676355
## [933,] 2.0426579 1.8595329
## [934,] 1.7386576 0.5958084
## [935,] 1.1496454 0.2358684
## [936,] 1.7468585 1.9488821
## [937,] 1.9264065 1.3434620
## [938,] 0.7164047 0.6779951
## [939,] 1.2738789 0.4462101
## [940,] 1.7310872 2.1168610
## [941,] 1.3538728 1.5235136
## [942,] 1.5599813 0.9691400
## [943,] 1.3243285 0.9422121
## [944,] 0.8639356 0.3694354
## [945,] 0.7146255 0.6606062
## [946,] 1.0115684 0.7496037
## [947,] 0.9213956 0.4255782
## [948,] 1.1378723 0.8560517
## [949,] 1.3275265 2.1810385
## [950,] 1.1477997 1.0120646
## [951,] 0.6967618 0.6100558
## [952,] 0.5578072 0.6216000
## [953,] 0.7405553 0.3102498
## [954,] 0.5705637 0.5106031
## [955,] 0.5142820 0.7053741
## [956,] 0.7047356 0.4248170
## [957,] 1.1754134 1.2774289
## [958,] 0.3332258 0.7335940
## [959,] 0.7120403 1.2160229
## [960,] 1.5087946 0.2823302
## [961,] 0.4903416 1.0958285
## [962,] 0.5004908 1.0891064
## [963,] 0.9389418 0.4255239
## [964,] 0.6168014 0.7755318
## [965,] 1.3034819 2.3649265
## [966,] 0.4487397 1.1360021
## [967,] 1.2674244 1.0664486
## [968,] 1.5720549 0.6919854
## [969,] 1.4310354 0.4513660
## [970,] 1.2368114 0.8792416
## [971,] 0.4804327 2.2200502
## [972,] 0.4186984 1.2531901
## [973,] 0.1218204 1.6404477
## [974,] 0.4654093 0.8988662
## [975,] 1.0986846 0.8368617
## [976,] 0.5698935 0.9670671
## [977,] 0.7725930 0.5185224
## [978,] 0.4062727 0.8478946
## [979,] 1.2007296 0.8209012
## [980,] 0.6305821 1.3164336
## [981,] 1.7051376 1.6385660
## [982,] 0.9498648 2.5324336
## [983,] 1.1589795 0.3622011
## [984,] 1.0845641 1.6226098
## [985,] 3.7830547 0.3845841
## [986,] 0.8440801 1.3649315
## [987,] 1.0566599 1.1398269
## [988,] 0.3845612 0.9261189
## [989,] 1.4414629 0.6286629
## [990,] 0.4720613 1.1402347
## [991,] 1.1868236 0.9437842
## [992,] 1.5059606 0.8569690
## [993,] 1.1354860 0.8517288
## [994,] 0.9749544 0.8880004
## [995,] 0.4320570 1.4335156
## [996,] 0.9551517 0.3304445
## [997,] 0.9104851 0.7420362
## [998,] 0.6270105 0.4702040
## [999,] 1.1531363 1.4686492
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 1 10
## 17 1 1 11
## 18 1 1 11
## 19 1 1 10
## 20 1 -1 0
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
##
## $terms
## pca_data_2023 ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_2023, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_2023 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist_2023 <- vegdist(pca_data_2023, species = "bray")
res_2023 <- pcoa(dist_2023)
p2 <- as.data.frame(res_2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_2023,.)
ggplot(p2, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
model02 <- betadisper(dist_2023, env_2023$microsite)
model02
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Density Open
## 0.4638 0.5348
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model02)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 0.2301
## Residuals 21 0.39782 0.018944
permutest(model02,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 99 0.22
## Residuals 21 0.39782 0.018944
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.26
## Open 0.23013
model02.HSD <- TukeyHSD(model02)
boxplot(model02)
model03 <- betadisper(dist_2023, env_2023$shrub_density)
model03
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.5348 0.3051 0.4393 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model03)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.71375 0.142751 4.86 0.006083 **
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model03,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.71375 0.142751 4.86 99 0.02 *
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.030000 0.150000
## 10 0.059818 0.380000
## 11 0.192393 0.435177
## 12
## 13
## 14
model03.HSD <- TukeyHSD(model03)
boxplot(model03)
model04 <- betadisper(dist_2023, env_2023$site)
model04
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.5093 0.4594 0.4488
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model04)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 0.6392
## Residuals 20 0.36181 0.0180906
permutest(model04,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 99 0.71
## Residuals 20 0.36181 0.0180906
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.66000 0.14
## Cuyama 0.56406 0.92
## Tecopa 0.14036 0.89377
model04.HSD <- TukeyHSD(model04)
boxplot(model04)
### From running the community compositions both in 2022 and 2023 it
seems the community compositions across densities, microsites, and sites
are all somewhat similar with no significant differences.
photo_final <- read.csv("observations_final.csv")
summary(photo_final)
## region site site_code microsite
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.000 Length:250716 Length:250716
## 1st Qu.:1.000 1st Qu.:1.000 Class :character Class :character
## Median :2.000 Median :1.000 Mode :character Mode :character
## Mean :1.772 Mean :1.498
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :2.000
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:250716
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 21744 Class :character
## Median :2023 Median :10.000 Median : 67344 Mode :character
## Mean :2023 Mean : 5.878 Mean : 77566
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:130022
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:250716 Length:250716 Min. :0.00000 Length:250716
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.01923
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:250716 Min. : 1
## Class :character 1st Qu.: 1
## Mode :character Median : 1
## Mean : 1
## 3rd Qu.: 1
## Max. :12
photo_final <- photo_final %>%
filter(common_name != "Human")
photo_final <- photo_final %>%
filter(common_name != "Human-Camera Trapper")
photo_final <- photo_final %>%
filter(common_name != "Domestic Dog")
photo_final <- photo_final %>%
filter(common_name != "Vehicle")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Insect")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Animal")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Bird")
count.hit_final <- photo_final %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_final)
## animal.hit n
## Min. :0.00 Min. : 3717
## 1st Qu.:0.25 1st Qu.: 64261
## Median :0.50 Median :124806
## Mean :0.50 Mean :124806
## 3rd Qu.:0.75 3rd Qu.:185350
## Max. :1.00 Max. :245894
# Wow we have a 1.489% capture rate for the project!
### Animal Observations by Site_Code
animals_by_sitecode_final <- photo_final%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_final <- animals_by_sitecode_final %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
animals_by_site_final <- photo_final %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_final <- animals_by_site_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
animals_by_density_final<- photo_final %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_final <- animals_by_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
Total_Observations_final <- photo_final %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") %>% filter(common_name != "Mammal")
density_obvs_final <- merge(animals_by_density_final, Total_Observations_final, all = TRUE) %>% filter(common_name != "Mammal")
density_obvs_final$percent_presence <- density_obvs_final$captures/density_obvs_final$total
m3<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_final)
anova(m3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## common_name 32 27791.1 22 0 < 2.2e-16 ***
## microsite:common_name 22 0.0 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(m3, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
#head(e3)
animals_density_final <- photo_final %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_final <- animals_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "Mammal")
pca_data_final <- animals_density_final ### Created new df for pca data
pca_data_final <- pca_data_final %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_final)
## [1] 24 35
env_final <- read.csv("environment_final.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env_final)
## [1] 24 5
model010 <- adonis(pca_data_final ~ microsite*shrub_density, data = env_final)
## 'adonis' will be deprecated: use 'adonis2' instead
model010
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2680 0.26799 1.0165 0.04244 0.350
## shrub_density 1 0.5102 0.51024 1.9354 0.08081 0.093 .
## Residuals 21 5.5363 0.26363 0.87675
## Total 23 6.3145 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = pca_data_final ~ microsite * shrub_density,
## data = env_final)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -10.557692 4.166667e-02 -11.346154
## microsite1 -14.057692 -4.166667e-02 -22.012821
## shrub_density 2.876923 9.476236e-19 4.661538
## microsite1:shrub_density NA NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.166667e-02 1.1185897
## microsite1 -4.166667e-02 1.0352564
## shrub_density 4.564605e-18 -0.1692308
## microsite1:shrub_density NA NA
## Bobcat Brewer's Blackbird
## (Intercept) -0.7019231 -2.4070513
## microsite1 -1.1185897 -2.8237179
## shrub_density 0.1692308 0.5538462
## microsite1:shrub_density NA NA
## California Ground Squirrel California Pocket Mouse
## (Intercept) 19.618590 0.33974359
## microsite1 13.868590 0.17307692
## shrub_density -1.769231 -0.03076923
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -6.185897 -1.0 2.5961538
## microsite1 -7.352564 -1.0 -1.9038462
## shrub_density 1.292308 0.2 0.1384615
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.2467949 -27.230769 1.958333e+00
## microsite1 -2.8365385 -28.897436 -1.958333e+00
## shrub_density 0.4153846 5.307692 3.783904e-16
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Great White Egret
## (Intercept) 6.8141026 4.166667e-02
## microsite1 4.6474359 -4.166667e-02
## shrub_density -0.7076923 4.564605e-18
## microsite1:shrub_density NA NA
## Greater Roadrunner Heermann's Kangaroo Rat Horned Lark
## (Intercept) -4.3814103 125.403846 0.38461538
## microsite1 -4.4647436 50.903846 0.38461538
## shrub_density 0.8307692 -4.538462 -0.04615385
## microsite1:shrub_density NA NA NA
## Killdeer Kit Fox Lark Sparrow
## (Intercept) 4.166667e-02 2.5288462 -1.8717949
## microsite1 -4.166667e-02 2.1121795 -2.2051282
## shrub_density 4.564605e-18 -0.3384615 0.3846154
## microsite1:shrub_density NA NA NA
## Loggerhead Shrike Merriam's Kangaroo Rat
## (Intercept) -1.1025641 1.06089744
## microsite1 -1.4358974 0.14423077
## shrub_density 0.2923077 -0.09230769
## microsite1:shrub_density NA NA
## Mohave Ground Squirrel Mourning Dove
## (Intercept) 2.916667e-01 -1.9551282
## microsite1 -2.916667e-01 -2.1217949
## shrub_density -1.570024e-16 0.3846154
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel No CV Result
## (Intercept) 18.894231 -0.8685897
## microsite1 13.977564 -0.9519231
## shrub_density -2.092308 0.1692308
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Say's Phoebe
## (Intercept) -0.5641026 -0.35256410 0.253205128
## microsite1 -0.5641026 -0.51923077 0.003205128
## shrub_density 0.1076923 0.09230769 -0.015384615
## microsite1:shrub_density NA NA NA
## Vesper Sparrow Western whiptail
## (Intercept) 1.5801282 0.12820513
## microsite1 1.4967949 0.12820513
## shrub_density -0.1846154 -0.01538462
## microsite1:shrub_density NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.33653846
## microsite1 -0.08012821
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.78080317 0.82434750 0.98306093 0.76605527
## microsite1 0.17504154 0.26134577 0.28849450 0.24337903
## shrub_density -0.03080745 -0.04609079 -0.04138812 -0.03373393
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.84838652 0.83124969 0.86997845 0.76272593
## microsite1 0.30313950 0.28542834 0.29904628 0.20499025
## shrub_density -0.05068255 -0.04597727 -0.04282439 -0.02976002
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7481015 1.5203179 1.00167997 1.0125329
## microsite1 1.0946409 0.9430041 0.41975528 0.4948288
## shrub_density -0.1923781 -0.1650103 -0.07142752 -0.0794049
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.88500919 0.84806327 0.8076157 0.83755826
## microsite1 0.18017241 0.07715532 0.2071152 0.27863228
## shrub_density -0.03799694 -0.02089895 -0.0336203 -0.04242666
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.04881892 0.31671402 0.25149293 0.07189331
## microsite1 -0.82473427 -0.56994858 -0.63539881 -0.82608604
## shrub_density 0.13158888 0.08613734 0.09735222 0.13310018
## microsite1:shrub_density NA NA NA NA
## 21 22 23 24
## (Intercept) 1.12355908 1.11712532 1.06379973 0.830255162
## microsite1 0.41343026 0.46257310 0.31720419 0.079719825
## shrub_density -0.06585209 -0.07418334 -0.04509589 -0.006831989
## microsite1:shrub_density NA NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.23496499 0.76822600
## [2,] 0.09262485 0.78962868
## [3,] 1.18384241 1.26968747
## [4,] 0.57125392 0.31991878
## [5,] 0.87128256 0.12714980
## [6,] 0.35392365 0.57198682
## [7,] 2.17368995 1.35872842
## [8,] 2.08285104 1.13426979
## [9,] 1.36433165 1.13533064
## [10,] 0.87152326 1.27718874
## [11,] 1.80510162 1.20285210
## [12,] 1.22711455 0.77486169
## [13,] 0.55485593 0.60529275
## [14,] 0.51329958 1.08130429
## [15,] 0.81333146 0.92130702
## [16,] 0.21106459 0.75207613
## [17,] 0.62200224 1.75191490
## [18,] 1.31580411 0.77065101
## [19,] 1.30913225 1.77719594
## [20,] 0.29381370 0.79190199
## [21,] 0.92878270 0.82689638
## [22,] 0.65787974 1.15645742
## [23,] 1.35407052 1.72986031
## [24,] 1.16148447 0.71405687
## [25,] 0.61306997 1.25917298
## [26,] 3.04091314 1.43482159
## [27,] 1.19171173 0.47147925
## [28,] 0.96115296 2.29478556
## [29,] 0.60245941 0.82175516
## [30,] 1.17128084 1.08757781
## [31,] 0.40921525 0.16592538
## [32,] 0.42484241 0.65679364
## [33,] 1.32529812 0.84093876
## [34,] 1.20355413 0.57478006
## [35,] 0.47409043 1.43136717
## [36,] 0.40839782 1.02353742
## [37,] 1.78289211 0.62306854
## [38,] 0.27561241 0.45059042
## [39,] 0.61379139 0.50412058
## [40,] 0.71339354 0.04957232
## [41,] 0.64191262 1.14814601
## [42,] 1.06574934 1.83195204
## [43,] 0.47305735 1.27191887
## [44,] 0.81681129 0.99825885
## [45,] 1.84026590 0.60303259
## [46,] 0.36552672 1.04491054
## [47,] 0.34691104 1.13974393
## [48,] 0.69445817 0.67169563
## [49,] 0.57180786 0.60790895
## [50,] 1.08892673 1.13312879
## [51,] 0.39566204 1.22019889
## [52,] 0.40118071 0.66226615
## [53,] 0.96721055 1.01216833
## [54,] 2.72519774 0.54672642
## [55,] 0.90114879 1.25828231
## [56,] 0.45828575 0.77411083
## [57,] 0.38296044 0.99896338
## [58,] 2.63183224 0.41259748
## [59,] 1.10815434 1.33958340
## [60,] 1.14829028 0.59348135
## [61,] 3.16349145 0.30148756
## [62,] 0.49226254 0.76437714
## [63,] 0.47147549 0.62199387
## [64,] 1.24956567 0.60280292
## [65,] 2.55197016 1.34633592
## [66,] 0.77672126 0.39741696
## [67,] 1.05819297 0.51260812
## [68,] 0.51481016 1.38842748
## [69,] 0.48936077 0.82768205
## [70,] 1.31595954 0.84850599
## [71,] 0.54312278 0.45900817
## [72,] 1.56946007 1.18525528
## [73,] 1.03203900 0.95503071
## [74,] 1.50534580 2.00793214
## [75,] 0.78320188 1.24361365
## [76,] 0.43910399 0.53117859
## [77,] 0.85296329 2.55225247
## [78,] 0.54418543 1.05499846
## [79,] 0.30065605 0.55230929
## [80,] 0.81400978 1.05495652
## [81,] 1.57200386 1.20741046
## [82,] 2.01193873 1.06802623
## [83,] 1.23898562 1.60966910
## [84,] 0.34608247 1.32901461
## [85,] 0.60540667 1.68122000
## [86,] 0.70090906 0.23643450
## [87,] 0.37018345 1.38374964
## [88,] 0.27402380 0.21760893
## [89,] 0.97201067 0.76773463
## [90,] 0.36969817 0.63978732
## [91,] 0.44285040 0.37010608
## [92,] 0.68820415 1.28244589
## [93,] 1.32181129 1.47178016
## [94,] 0.70340487 1.85184083
## [95,] 1.22137542 1.15580348
## [96,] 1.07909015 1.18784623
## [97,] 2.59015383 0.43232040
## [98,] 0.35477374 0.89806208
## [99,] 0.54387297 0.25645687
## [100,] 1.02867024 0.71884632
## [101,] 0.70688312 1.21693517
## [102,] 0.96991024 0.33943583
## [103,] 0.67086163 0.56789582
## [104,] 0.99954014 1.52061233
## [105,] 2.86087470 0.51774225
## [106,] 0.78329801 0.26357793
## [107,] 1.95279825 1.28736227
## [108,] 2.52627037 1.07544489
## [109,] 0.75917183 0.96515201
## [110,] 1.47933680 0.76925569
## [111,] 1.07448800 0.83606625
## [112,] 0.61778563 0.86103001
## [113,] 1.01175732 1.05922976
## [114,] 1.04001402 0.80499863
## [115,] 0.36784659 2.18958840
## [116,] 0.35349119 0.47400929
## [117,] 0.92024277 3.02365204
## [118,] 0.49843473 1.29407481
## [119,] 0.16519736 1.02703752
## [120,] 3.60215479 4.09922213
## [121,] 0.64748969 0.84909117
## [122,] 0.97227726 0.46225445
## [123,] 0.85779803 1.37842307
## [124,] 0.41284416 1.05877495
## [125,] 1.43512238 0.90422665
## [126,] 0.63549206 0.63588864
## [127,] 2.25620928 1.00807733
## [128,] 0.68819317 1.09436543
## [129,] 0.66003448 1.26129678
## [130,] 0.55465950 0.34856940
## [131,] 0.67452428 1.08348688
## [132,] 0.37027455 1.40899783
## [133,] 0.84616144 1.76434489
## [134,] 0.96254284 0.84543650
## [135,] 0.94278679 0.42203839
## [136,] 0.61415909 1.09665447
## [137,] 0.72564851 1.02409447
## [138,] 1.81477909 0.57145896
## [139,] 0.30017708 1.00338418
## [140,] 0.76325194 0.95073779
## [141,] 1.74536118 0.19128239
## [142,] 2.38025595 0.53772008
## [143,] 0.58619960 0.39404703
## [144,] 1.08766112 0.57801442
## [145,] 1.19151084 1.68009279
## [146,] 1.81772224 0.26932175
## [147,] 0.57721586 0.54115892
## [148,] 1.44656976 0.35487447
## [149,] 0.32281866 0.34805550
## [150,] 1.13899089 0.77161896
## [151,] 0.90746361 0.72461985
## [152,] 1.27903982 1.69308437
## [153,] 0.38856981 0.19946582
## [154,] 0.71491349 0.37221828
## [155,] 0.83061118 0.30289671
## [156,] 0.57332442 0.46858393
## [157,] 2.13079914 0.77731826
## [158,] 1.82279217 0.38564811
## [159,] 0.81474769 1.28368930
## [160,] 0.66715899 0.25442281
## [161,] 1.50063894 0.69963219
## [162,] 2.69875437 0.75400009
## [163,] 1.94894501 3.03488354
## [164,] 0.26210100 0.98694408
## [165,] 0.57075177 0.60806065
## [166,] 0.37860271 1.37008547
## [167,] 1.11665152 1.14622578
## [168,] 0.59338012 0.54393255
## [169,] 0.82346503 0.74708163
## [170,] 0.98027952 1.43347113
## [171,] 0.67424219 1.08330196
## [172,] 0.20376826 0.81721116
## [173,] 0.53075044 0.48261081
## [174,] 0.84099286 0.67146963
## [175,] 0.77816047 1.07351881
## [176,] 0.49296191 2.23734317
## [177,] 1.45006564 1.16440119
## [178,] 0.79602786 0.61307255
## [179,] 0.44377160 0.63529680
## [180,] 0.74583253 1.13846282
## [181,] 1.87256788 0.89777274
## [182,] 0.55513451 0.66704391
## [183,] 0.79187215 0.59276757
## [184,] 0.52393202 0.97500224
## [185,] 1.03140071 0.56333618
## [186,] 1.08411182 0.62303549
## [187,] 0.47003035 0.33991961
## [188,] 0.66233133 0.84642238
## [189,] 1.25907307 0.92713518
## [190,] 1.58721954 1.84937902
## [191,] 1.65703053 0.50157984
## [192,] 1.69792511 1.32153299
## [193,] 1.12405265 0.75721310
## [194,] 0.15001982 0.90113233
## [195,] 0.95859902 1.15299575
## [196,] 0.62385738 1.55647744
## [197,] 2.32738051 1.18106587
## [198,] 0.94397100 0.92355499
## [199,] 1.42815390 0.59202929
## [200,] 1.07149797 1.08039971
## [201,] 0.30295843 0.22647207
## [202,] 0.92065095 0.22075479
## [203,] 0.60398728 0.59814691
## [204,] 1.00086191 1.25091170
## [205,] 0.90859917 0.42034767
## [206,] 0.55287135 1.12024521
## [207,] 1.04847958 0.39442770
## [208,] 1.04345929 1.99764923
## [209,] 3.86803730 2.13959800
## [210,] 0.36993936 2.38978789
## [211,] 0.17238469 0.79280446
## [212,] 1.01591937 0.94828480
## [213,] 1.88663580 0.65076264
## [214,] 1.01205722 0.97782396
## [215,] 0.95497428 0.14763276
## [216,] 1.01802204 0.73677650
## [217,] 0.65711066 2.46777280
## [218,] 1.10046370 1.23546395
## [219,] 0.51448353 2.07089544
## [220,] 0.54101344 1.48541975
## [221,] 1.44514787 1.50623721
## [222,] 0.82863072 1.08739610
## [223,] 0.79319000 1.48236777
## [224,] 3.50382285 0.10063593
## [225,] 1.00023433 2.01904878
## [226,] 0.83598186 0.92834611
## [227,] 1.81025856 1.41169606
## [228,] 0.62484540 1.49006575
## [229,] 0.30484097 1.14592517
## [230,] 0.90203826 0.66393283
## [231,] 0.51609709 0.59620228
## [232,] 1.56121546 2.01887089
## [233,] 2.47703657 3.54717428
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## [687,] 0.76845932 1.48443904
## [688,] 0.62267510 0.68304938
## [689,] 0.25479680 0.21944705
## [690,] 1.06405707 0.68326828
## [691,] 0.68924236 1.21475291
## [692,] 0.84256625 1.28021901
## [693,] 0.53946011 0.57769444
## [694,] 0.45100453 2.86171450
## [695,] 0.70855891 1.97806735
## [696,] 0.95381545 0.72539621
## [697,] 1.20127882 1.20140745
## [698,] 0.73415845 1.90892220
## [699,] 0.79285384 1.11965155
## [700,] 1.46917717 0.45372717
## [701,] 1.04448632 1.52458807
## [702,] 0.36200794 0.65525235
## [703,] 0.51016703 0.72457236
## [704,] 0.32267376 0.55315977
## [705,] 1.71697070 0.50234691
## [706,] 1.42427196 0.59650605
## [707,] 1.01681958 0.65872191
## [708,] 0.55341236 2.62275562
## [709,] 1.38433121 1.28155355
## [710,] 0.46726267 0.37181688
## [711,] 0.43250038 0.85676962
## [712,] 0.54244318 1.26185221
## [713,] 0.65482474 0.98966029
## [714,] 0.80213455 1.20133293
## [715,] 0.60410980 1.36363528
## [716,] 0.60929269 2.74517755
## [717,] 0.65998731 0.83682773
## [718,] 0.76203787 0.55455904
## [719,] 0.43005870 2.27324622
## [720,] 1.47500320 2.61622652
## [721,] 1.48985505 1.56574167
## [722,] 1.48218712 1.30734662
## [723,] 0.53804585 1.96204754
## [724,] 1.74866266 4.64455458
## [725,] 1.21054205 0.40177484
## [726,] 1.96617420 0.77683058
## [727,] 0.31311542 0.72744547
## [728,] 0.30945494 0.58905209
## [729,] 0.58203729 1.14435408
## [730,] 0.35677416 0.86376471
## [731,] 0.35443035 0.42842719
## [732,] 0.64878701 1.27751249
## [733,] 0.53091687 1.43948616
## [734,] 0.85034167 1.32671994
## [735,] 0.39863345 0.60843373
## [736,] 0.81170410 1.36827245
## [737,] 0.67009454 0.69287715
## [738,] 0.42731974 0.63331259
## [739,] 1.32245967 1.91043195
## [740,] 0.51628874 1.50069308
## [741,] 0.71002896 2.17552440
## [742,] 1.28789491 0.50262546
## [743,] 0.58077105 1.80107869
## [744,] 0.73495813 0.92168058
## [745,] 0.88998802 0.53295886
## [746,] 0.34928720 0.85904279
## [747,] 0.28059664 0.66313503
## [748,] 0.80969302 0.48213325
## [749,] 1.01776289 0.85409912
## [750,] 0.85740671 0.99809637
## [751,] 0.73489000 0.44938612
## [752,] 0.92373100 0.42111202
## [753,] 1.47178927 0.79670651
## [754,] 0.82943494 1.18176641
## [755,] 0.71080895 0.34088550
## [756,] 1.13784285 1.33457515
## [757,] 0.83571188 1.10493060
## [758,] 1.57958734 0.78321726
## [759,] 0.42345294 0.92130321
## [760,] 0.62940449 0.72548098
## [761,] 1.30814866 2.33205705
## [762,] 0.77454054 1.10292536
## [763,] 1.40756403 0.99642037
## [764,] 1.45325955 1.53280667
## [765,] 1.01501318 2.77405336
## [766,] 1.06762095 1.38065706
## [767,] 0.52819477 0.57855194
## [768,] 0.60428598 0.50227216
## [769,] 0.80023874 1.44258724
## [770,] 0.35461246 0.54786371
## [771,] 0.56235950 1.52272423
## [772,] 0.47816371 0.61243980
## [773,] 1.55810647 1.14383038
## [774,] 1.11052846 1.02813544
## [775,] 0.66649858 0.28801534
## [776,] 0.47402627 0.91131822
## [777,] 1.23882068 1.15409747
## [778,] 1.45707330 1.33660120
## [779,] 1.90279063 1.33280970
## [780,] 1.54804185 0.59218224
## [781,] 1.10098209 1.76421618
## [782,] 0.50706905 2.91422902
## [783,] 0.71496634 0.22146741
## [784,] 0.98307332 2.50810954
## [785,] 1.09457465 1.17186721
## [786,] 0.61594502 1.08426061
## [787,] 0.93406629 1.00113049
## [788,] 0.84253889 0.22527246
## [789,] 1.55665835 2.70224164
## [790,] 1.44649807 1.90201942
## [791,] 1.11619320 1.06751457
## [792,] 0.09383895 0.61235674
## [793,] 1.47135751 0.60098051
## [794,] 1.58005465 0.31584613
## [795,] 1.55739207 0.65692307
## [796,] 2.29164676 0.55058918
## [797,] 0.93058450 0.66693521
## [798,] 0.73500205 0.58323908
## [799,] 0.67446368 0.94021808
## [800,] 1.08547387 1.37633270
## [801,] 0.68245483 0.69545837
## [802,] 0.50179757 0.62475886
## [803,] 0.61910098 0.73677167
## [804,] 1.49026500 0.80969062
## [805,] 1.45179297 0.89987098
## [806,] 0.54740790 0.58132030
## [807,] 0.86205726 0.85138191
## [808,] 0.59083511 0.52479279
## [809,] 0.85112129 1.36740351
## [810,] 0.70658559 0.97233147
## [811,] 0.81253622 1.15041181
## [812,] 0.41450782 1.41524205
## [813,] 0.82091427 0.37259115
## [814,] 0.52376194 1.41685258
## [815,] 0.77114039 1.60862863
## [816,] 1.22465591 0.59073340
## [817,] 0.95781654 0.95271945
## [818,] 0.63472191 1.11115609
## [819,] 0.76358623 1.06704974
## [820,] 0.59707072 0.71513485
## [821,] 0.85871503 2.37412511
## [822,] 0.64158975 0.80347374
## [823,] 1.55205644 0.35447264
## [824,] 0.90838222 0.61772986
## [825,] 3.51577286 1.58348915
## [826,] 0.33166547 2.33653491
## [827,] 0.63901842 1.02534814
## [828,] 0.31128520 5.41353508
## [829,] 0.51843339 0.97257635
## [830,] 0.55436876 0.61379923
## [831,] 0.58574315 1.21869541
## [832,] 0.29169072 1.28705874
## [833,] 1.86278887 0.71840808
## [834,] 0.42041615 0.79215627
## [835,] 1.02407234 0.73595143
## [836,] 2.00215929 0.92939937
## [837,] 0.44559268 0.71634208
## [838,] 0.89504085 1.65689160
## [839,] 1.02535354 1.09895574
## [840,] 0.60917067 1.82510686
## [841,] 0.69057386 0.26323949
## [842,] 0.43071677 1.19993542
## [843,] 1.30753005 0.95535958
## [844,] 0.13777656 1.44846464
## [845,] 0.92258916 0.55621499
## [846,] 0.44873757 1.04286950
## [847,] 0.64279155 0.79864140
## [848,] 2.08286281 0.59351539
## [849,] 0.71094494 1.01808404
## [850,] 0.48195024 0.93288414
## [851,] 1.80405854 0.87047101
## [852,] 2.52854446 1.11100840
## [853,] 1.21928920 0.70995115
## [854,] 3.96601672 0.50853007
## [855,] 2.14479592 1.87646838
## [856,] 1.90412609 0.18533800
## [857,] 1.85090905 3.00966631
## [858,] 0.67214844 4.65391294
## [859,] 0.70156410 1.01428882
## [860,] 0.29351715 0.37085724
## [861,] 1.32320359 0.47168272
## [862,] 1.27413952 0.55207094
## [863,] 0.77478015 1.98764433
## [864,] 3.09711533 1.80370131
## [865,] 1.01547136 0.74631698
## [866,] 0.82466584 0.21102808
## [867,] 0.61803730 2.36629347
## [868,] 3.46854118 1.11793801
## [869,] 1.09256390 0.78005281
## [870,] 0.54059526 0.50013181
## [871,] 0.32085582 0.60466249
## [872,] 1.05916034 1.70654660
## [873,] 1.13585932 1.12630061
## [874,] 2.21729981 0.58453025
## [875,] 0.68275123 0.29496890
## [876,] 2.34421459 0.75510598
## [877,] 0.96660792 0.76149712
## [878,] 0.37156579 0.69832164
## [879,] 0.92663880 0.47332541
## [880,] 0.31474644 0.62203251
## [881,] 0.66612502 1.38598333
## [882,] 0.79651824 0.67247826
## [883,] 1.87018804 0.13356866
## [884,] 0.45754262 0.88423297
## [885,] 0.65014849 0.47032362
## [886,] 0.77285185 0.86075659
## [887,] 0.56272327 1.06181624
## [888,] 0.44374699 2.60851317
## [889,] 2.04456279 0.59525391
## [890,] 1.13371743 0.67189767
## [891,] 1.46390299 1.77383855
## [892,] 0.13512929 1.72057250
## [893,] 1.68376913 1.00940365
## [894,] 0.34731932 1.01305001
## [895,] 0.65650483 0.52295558
## [896,] 0.63410093 0.75147133
## [897,] 0.43276531 0.55611830
## [898,] 1.17889584 1.29308886
## [899,] 0.67801612 1.09922046
## [900,] 1.48300657 0.97460796
## [901,] 1.25386211 0.38496759
## [902,] 0.61074999 1.96542454
## [903,] 0.82498370 0.76577511
## [904,] 0.30190158 0.58061735
## [905,] 0.54192490 0.32129647
## [906,] 0.96739136 0.65171137
## [907,] 1.20244357 0.54517471
## [908,] 1.04092612 0.90224335
## [909,] 0.48427322 1.70539349
## [910,] 0.37291688 0.60446502
## [911,] 0.54586214 0.75045415
## [912,] 0.90915348 0.30176811
## [913,] 0.93397569 0.45377279
## [914,] 1.23809691 0.67378531
## [915,] 0.48201505 0.70064244
## [916,] 0.84143006 0.80556158
## [917,] 0.83494952 0.48739706
## [918,] 0.79742960 0.33584395
## [919,] 3.93054649 0.83905524
## [920,] 0.87783594 0.43348257
## [921,] 0.74400258 0.63340826
## [922,] 0.32285320 0.81455291
## [923,] 0.41764934 0.72273306
## [924,] 1.75716863 1.56628687
## [925,] 0.54933241 1.35605563
## [926,] 0.40654202 0.79134487
## [927,] 0.63489621 1.53863858
## [928,] 0.51119648 0.29266433
## [929,] 1.68831206 0.47220610
## [930,] 0.49666141 0.61521745
## [931,] 0.65066219 2.64083693
## [932,] 1.04086558 1.02466130
## [933,] 1.32745423 1.53111315
## [934,] 1.64521405 1.40051272
## [935,] 0.96523879 2.70010459
## [936,] 0.76993862 0.98871002
## [937,] 0.52338568 0.54784253
## [938,] 1.09644134 1.51289873
## [939,] 2.53652953 0.81627510
## [940,] 0.75051629 0.35489278
## [941,] 0.78647710 0.44349760
## [942,] 1.45006728 1.60062425
## [943,] 0.65091180 0.42958424
## [944,] 0.53840189 0.58103261
## [945,] 1.39281934 2.05964152
## [946,] 2.65325498 0.52977175
## [947,] 0.82029603 1.00323597
## [948,] 1.44925904 0.18480353
## [949,] 0.52661522 0.74925140
## [950,] 0.71190988 1.90055415
## [951,] 0.58813074 0.64582426
## [952,] 0.45174605 0.22329388
## [953,] 0.87243712 2.15840187
## [954,] 1.69847305 1.41647186
## [955,] 0.57155878 0.57540625
## [956,] 0.46530578 1.05019750
## [957,] 0.90364653 0.91433561
## [958,] 1.49305829 1.07958841
## [959,] 1.16296979 0.55226303
## [960,] 2.66687101 0.25149711
## [961,] 0.48123571 0.37420663
## [962,] 0.73301988 0.52189599
## [963,] 0.67200389 0.46139033
## [964,] 1.05429176 0.53024480
## [965,] 0.49128965 2.35295215
## [966,] 1.35870391 0.48767385
## [967,] 0.74061542 1.94095858
## [968,] 1.33282109 1.09400639
## [969,] 1.74403759 2.33643764
## [970,] 0.25952708 0.61846903
## [971,] 0.53501305 0.46448707
## [972,] 0.59442498 1.13909007
## [973,] 0.47885396 0.16679348
## [974,] 1.34537240 1.19695725
## [975,] 0.44915420 0.82809682
## [976,] 0.64168193 0.52860041
## [977,] 1.33925002 3.03503864
## [978,] 0.32839663 1.76664399
## [979,] 0.98462510 0.85480343
## [980,] 1.50052079 0.52674144
## [981,] 0.75205232 0.71317244
## [982,] 0.39176798 1.71079144
## [983,] 0.44813997 0.18411487
## [984,] 0.47527169 2.64295171
## [985,] 1.66440225 1.57954578
## [986,] 0.58575582 1.06720555
## [987,] 1.39610481 0.42259281
## [988,] 2.61574154 1.33563405
## [989,] 0.98945853 0.96495544
## [990,] 0.87999551 1.14208855
## [991,] 1.02656861 1.18866014
## [992,] 1.53156084 1.50200472
## [993,] 0.61494588 0.60978612
## [994,] 0.86038921 0.64911447
## [995,] 0.87326882 1.01885810
## [996,] 0.79084062 0.76181365
## [997,] 1.04971104 0.36455902
## [998,] 1.00068603 0.55395156
## [999,] 0.91927612 0.56423221
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
## 24 1 -1 0
##
## $terms
## pca_data_final ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_final, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_final 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist_final <- vegdist(pca_data_final, species = "bray")
res_final <- pcoa(dist_final)
p02 <- as.data.frame(res_final$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_final,.)
p02$microsite <- ifelse(p02$microsite == "Density", "Shrub", p02$microsite)
pcoa_final <- ggplot(p02, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
labs(color = "Microsite")
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pcoa_final <- pcoa_final + labs(x = "Shrub Density Gradient", y = "Community Composition") + scale_color_manual(
values = c("Shrub" = "#009900", "Open" = "#0066cc"), # Change color codes here
labels = c("Shrub" = "Shrub", "Open" = "Open")) + coord_fixed(ratio = 1)
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
pcoa_final
model020 <- betadisper(dist_final, env_final$microsite)
model020
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$microsite)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5034 0.4680
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model020)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 0.6184
## Residuals 22 0.64591 0.0293594
permutest(model020,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 99 0.67
## Residuals 22 0.64591 0.0293594
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.66
## Open 0.6184
model020.HSD <- TukeyHSD(model020)
model020.HSD
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## Open-Density -0.03534393 -0.1804147 0.1097268 0.6183989
boxplot(model020)
model030 <- betadisper(dist_final, env_final$shrub_density)
model030
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$shrub_density)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4680 0.3766 0.4847 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model030)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.58003 0.11601 2.233 0.09554 .
## Residuals 18 0.93511 0.05195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model030,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.58003 0.11601 2.233 99 0.1
## Residuals 18 0.93511 0.05195
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.59000 0.82000
## 10 0.55757 0.66000
## 11 0.86321 0.60937
## 12
## 13
## 14
model030.HSD <- TukeyHSD(model030)
boxplot(model030)
### Shows a significant difference between community composition of tested sites (Carrizo -> Cuyama -> Tecopa)
model040 <- betadisper(dist_final, env_final$site)
model040
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$site)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2734 0.3349 0.4558
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model040)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 0.04091 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model040,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 99 0.03 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.410000 0.01
## Cuyama 0.425689 0.12
## Tecopa 0.016550 0.067329
model040.HSD <- TukeyHSD(model040)
supp_plot <- boxplot(model040, xlab = "Region")
### Temperature Data 2022
Temp_2022 <- read_csv("Temp Data 2022.csv")
## Rows: 52443 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): researcher, site, site_code, microsite, time_block
## dbl (5): microsite_number, pendent_number, pendent_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2022_final <- Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
###Ground Temp Data
Ground_Temp_2022 <- read_csv("Gradient Density Datasheet 2022.csv")
## Rows: 695 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): site, site_code, date, microsite
## dbl (22): rep, microsite_number, shrub_ID, shrub_number, total_shrub, micros...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ground_Temp_2022_final <- Ground_Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
### Combine this with new_data
### Aridity Data
aridity <- read_csv("regional_sites_2022.csv")
## Rows: 51 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): state, desert, region, experiment, site_acronym, sub_site, site_co...
## dbl (7): lat, long, elevation, MAT, MAP, aridity, area_block_m2
## num (1): area_m2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
aridity <- aridity %>%
dplyr::select(site_code, aridity)
final_2022<- photo%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
final_2022 <- final_2022 %>%
filter(common_name != "Blank")%>% filter(common_name != "No CV Result")
density_simple <- final_2022 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
#write.csv(density_simple, file = "density_simple_fixed.csv") Output density simple because it does not include the sites with 0 observations
density_simple_fixed <- read.csv("density_simple_fixed.csv")
density_simple_fixed <- density_simple_fixed[,-1]
### This is for 2022 evenness
vegan_data <- animals_density ### Created new df for pca data
vegan_data <- vegan_data %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data <- vegan_data %>%
group_by(site_code, microsite) %>%
summarize(across(5:29, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data <- na.omit(evenness_data)
evenness_data <- evenness_data %>%
mutate(Average_Evenness = rowMeans(across(5:26)))
evenness_data <- evenness_data %>%
dplyr::select(site_code, microsite, Average_Evenness)
### Join evenness_data with density_simple data
new_data <- inner_join(density_simple_fixed, evenness_data, by = c("site_code", "microsite"))
### Combine new data with logger temp data from 2022
new_data <- inner_join(new_data, Temp_2022_final,by = c("site_code", "microsite"))
### Need to add ground temperature from hand recordings then 2022 is ready!
new_data <- inner_join(new_data, Ground_Temp_2022_final, by = c("site_code", "microsite"))
### Combine all data with aridity data of the sites we have
new_data <- inner_join(new_data, aridity, by = c("site_code"))
### 2022 data is now cleaned and ready to go
### Clean up 2023 data so it can be properly combined with 2022 data
final_2023<- photo_2023%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
density_simple_2023 <- final_2023 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
write.csv(density_simple_2023, file = "density_simple_fixed_2023.csv")
### This is for 2023 evenness
vegan_data_2023 <- animals_density_2023 ### Created new df for pca data
vegan_data_2023 <- vegan_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data_2023 <- vegan_data_2023 %>%
group_by(site_code, microsite) %>%
summarize(across(5:27, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data_2023 <- na.omit(evenness_data_2023)
evenness_data_2023 <- evenness_data_2023 %>%
mutate(Average_Evenness = rowMeans(across(5:24)))
evenness_data_2023 <- evenness_data_2023 %>%
dplyr::select(site_code, microsite, Average_Evenness)
new_data_2023 <- inner_join(density_simple_2023, evenness_data_2023, by = c("site_code", "microsite"))
new_data_2023$site_code <- gsub("Tecopa_Shrub", "Tecopa_shrub", new_data_2023$site_code)
new_data_2023$site_code <- gsub("Tecopa_Open", "Tecopa_open", new_data_2023$site_code)
### Follow same steps as above 2022 data clean up. Start with logger temp, then ground temp, then aridity.
### Combine new_data_2023 to Temp_2023
Temp_2023 <- read_csv("Temp Data 2023.csv") %>% filter(!(temp > 50)) %>% filter(!(temp < -47))
## Rows: 24760 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): researcher, site, site_code, microsite
## dbl (5): microsite_number, pendant_number, pendant_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2023_final <- Temp_2023 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Temp_2023_final, by = c("site_code", "microsite"))
### Combine Ground Temp Data from 2023
Ground_Temp_2023 <- read_csv("Gradient Density Datasheet 2023.csv")
## New names:
## Rows: 587 Columns: 29
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): site, site_code, date, microsite dbl (25): rep, microsite_number,
## shrub_ID, shrub_number, total_shrub, micros...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...28`
## • `` -> `...29`
Ground_Temp_2023_final <- Ground_Temp_2023 %>%
group_by(site_code, microsite, microsite_number) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp))
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Ground_Temp_2023_final, by = c("site_code", "microsite", "microsite_number"))
### Aridity data is already set up from 2022 data
new_data_2023 <- inner_join(new_data_2023, aridity, by = c("site_code"))
### 2023 Data is now cleaned and ready to be combined with 2022 data
final_data <- rbind(new_data, new_data_2023)
library(ggpubr)
shapiro.test(final_data$animals)
##
## Shapiro-Wilk normality test
##
## data: final_data$animals
## W = 0.62948, p-value = 1.551e-09
ggqqplot(final_data$animals)
### Stats to show that the aridity across tested regions significantly varies. This needs to be one of the FIRST things you put in your results.
anova_result <- aov(aridity ~ site, data = final_data)
summary(anova_result)
## Df Sum Sq Mean Sq F value Pr(>F)
## site 2 103.52 51.76 3639 <2e-16 ***
## Residuals 43 0.61 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(anova_result, pairwise ~ site)
## $emmeans
## site emmean SE df lower.CL upper.CL
## Carrizo 3.252 0.0319 43 3.188 3.316
## Cuyama 3.701 0.0298 43 3.641 3.762
## Tecopa 0.365 0.0298 43 0.305 0.425
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo - Cuyama -0.449 0.0436 43 -10.297 <.0001
## Carrizo - Tecopa 2.887 0.0436 43 66.147 <.0001
## Cuyama - Tecopa 3.336 0.0422 43 79.126 <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimates
tukey_result <- TukeyHSD(anova_result)
# View the Tukey's HSD results
print(tukey_result)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = aridity ~ site, data = final_data)
##
## $site
## diff lwr upr p adj
## Cuyama-Carrizo 0.4494073 0.3434585 0.5553561 0
## Tecopa-Carrizo -2.8870527 -2.9930014 -2.7811039 0
## Tecopa-Cuyama -3.3364600 -3.4388162 -3.2341037 0
### Tukey test shows Cuyama less arid than Carrizo, Carrizo less arid than Tecopa, Cuyama less arid than Tecopa
### Abundance
model1 <- glm(animals~shrub_density*site*year+aridity, family = "gaussian", data = final_data)
model1
##
## Call: glm(formula = animals ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 7.551e+04 1.061e+04
## siteCuyama siteTecopa
## 4.027e+05 -9.065e+04
## year aridity
## -3.736e+01 3.297e+01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -1.559e+03 -1.211e+04
## shrub_density:year siteCuyama:year
## -5.245e+00 -1.991e+02
## siteTecopa:year shrub_density:siteCuyama:year
## 4.485e+01 7.703e-01
## shrub_density:siteTecopa:year
## 5.986e+00
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 765800
## Residual Deviance: 302900 AIC: 563
anova(model1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## shrub_density 1 18242 44 747531 0.1586319
## site 2 139832 42 607700 0.0004923 ***
## year 1 137687 41 470013 0.0001076 ***
## aridity 1 55 40 469958 0.9380678
## shrub_density:site 2 3544 38 466414 0.8244495
## shrub_density:year 1 5580 37 460833 0.4355873
## site:year 2 155562 35 305272 0.0002090 ***
## shrub_density:site:year 2 2342 33 302929 0.8802245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(model1, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e2
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 83.8 122 33 -164 332
## Cuyama 2022 235.1 176 33 -123 593
## Tecopa 2022 74.6 274 33 -482 631
## Carrizo 2023 15.7 111 33 -210 242
## Cuyama 2023 -27.6 176 33 -386 330
## Tecopa 2023 86.4 274 33 -470 643
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -151.28 73.5 33 -2.057 0.3336
## Carrizo year2022 - Tecopa year2022 9.25 391.4 33 0.024 1.0000
## Carrizo year2022 - Carrizo year2023 68.15 54.7 33 1.246 0.8109
## Carrizo year2022 - Cuyama year2023 111.45 73.5 33 1.516 0.6570
## Carrizo year2022 - Tecopa year2023 -2.59 391.4 33 -0.007 1.0000
## Cuyama year2022 - Tecopa year2022 160.53 446.7 33 0.359 0.9991
## Cuyama year2022 - Carrizo year2023 219.43 87.3 33 2.515 0.1490
## Cuyama year2022 - Cuyama year2023 262.73 47.9 33 5.480 0.0001
## Cuyama year2022 - Tecopa year2023 148.70 446.7 33 0.333 0.9994
## Tecopa year2022 - Carrizo year2023 58.90 378.5 33 0.156 1.0000
## Tecopa year2022 - Cuyama year2023 102.20 446.7 33 0.229 0.9999
## Tecopa year2022 - Tecopa year2023 -11.83 48.2 33 -0.245 0.9999
## Carrizo year2023 - Cuyama year2023 43.30 87.3 33 0.496 0.9960
## Carrizo year2023 - Tecopa year2023 -70.73 378.5 33 -0.187 1.0000
## Cuyama year2023 - Tecopa year2023 -114.04 446.7 33 -0.255 0.9998
##
## P value adjustment: tukey method for comparing a family of 6 estimates
shapiro.test(final_data$richness)
##
## Shapiro-Wilk normality test
##
## data: final_data$richness
## W = 0.96052, p-value = 0.1201
ggqqplot(final_data$richness)
### Richness Stats
model2 <- glm(richness~shrub_density*site*year +aridity, family = "gaussian", data = final_data)
model2
##
## Call: glm(formula = richness ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## -1.049e+03 -9.984e+01
## siteCuyama siteTecopa
## 1.799e+04 -6.515e+03
## year aridity
## 5.227e-01 -5.391e-01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -2.708e+02 -1.922e+02
## shrub_density:year siteCuyama:year
## 4.939e-02 -8.895e+00
## siteTecopa:year shrub_density:siteCuyama:year
## 3.219e+00 1.339e-01
## shrub_density:siteTecopa:year
## 9.508e-02
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 575.9
## Residual Deviance: 79.32 AIC: 183.6
anova_results2 <- aov(model2, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## shrub_density 1 27.132 44 548.78 0.0007803 ***
## site 2 164.760 42 384.02 1.305e-15 ***
## year 1 6.218 41 377.80 0.1077656
## aridity 1 1.307 40 376.50 0.4609209
## shrub_density:site 2 1.941 38 374.56 0.6678024
## shrub_density:year 1 3.261 37 371.29 0.2441039
## site:year 2 290.938 35 80.36 < 2.2e-16 ***
## shrub_density:site:year 2 1.034 33 79.32 0.8064261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results2)
## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 27.13 27.13 11.287 0.00198 **
## site 2 164.76 82.38 34.272 8.82e-09 ***
## year 1 6.22 6.22 2.587 0.11729
## aridity 1 1.31 1.31 0.544 0.46613
## shrub_density:site 2 1.94 0.97 0.404 0.67106
## shrub_density:year 1 3.26 3.26 1.357 0.25246
## site:year 2 290.94 145.47 60.519 9.11e-12 ***
## shrub_density:site:year 2 1.03 0.52 0.215 0.80755
## Residuals 33 79.32 2.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(model2, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e3 ### Ok now this all works.
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 6.356 1.97 33 2.346 10.37
## Cuyama 2022 12.443 2.85 33 6.649 18.24
## Tecopa 2022 0.208 4.43 33 -8.799 9.21
## Carrizo 2023 7.168 1.80 33 3.513 10.82
## Cuyama 2023 5.146 2.85 33 -0.648 10.94
## Tecopa 2023 4.797 4.43 33 -4.209 13.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -6.087 1.190 33 -5.116 0.0002
## Carrizo year2022 - Tecopa year2022 6.148 6.333 33 0.971 0.9239
## Carrizo year2022 - Carrizo year2023 -0.813 0.885 33 -0.918 0.9390
## Carrizo year2022 - Cuyama year2023 1.210 1.190 33 1.017 0.9090
## Carrizo year2022 - Tecopa year2023 1.558 6.333 33 0.246 0.9999
## Cuyama year2022 - Tecopa year2022 12.235 7.229 33 1.692 0.5461
## Cuyama year2022 - Carrizo year2023 5.274 1.412 33 3.736 0.0085
## Cuyama year2022 - Cuyama year2023 7.297 0.776 33 9.405 <.0001
## Cuyama year2022 - Tecopa year2023 7.645 7.229 33 1.058 0.8944
## Tecopa year2022 - Carrizo year2023 -6.960 6.125 33 -1.136 0.8626
## Tecopa year2022 - Cuyama year2023 -4.938 7.229 33 -0.683 0.9826
## Tecopa year2022 - Tecopa year2023 -4.590 0.781 33 -5.880 <.0001
## Carrizo year2023 - Cuyama year2023 2.022 1.412 33 1.432 0.7076
## Carrizo year2023 - Tecopa year2023 2.371 6.125 33 0.387 0.9988
## Cuyama year2023 - Tecopa year2023 0.348 7.229 33 0.048 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates
ggqqplot(final_data$Average_Evenness)
### Evenness
model3 <- glm(Average_Evenness~shrub_density*site*year + aridity, family = "gaussian", data = final_data)
model3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * site * year +
## aridity, family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 1.175e+02 -8.606e+00
## siteCuyama siteTecopa
## 2.013e+02 -3.866e+02
## year aridity
## -5.801e-02 -2.568e-02
## shrub_density:siteCuyama shrub_density:siteTecopa
## 1.008e+01 -9.806e+00
## shrub_density:year siteCuyama:year
## 4.257e-03 -9.952e-02
## siteTecopa:year shrub_density:siteCuyama:year
## 1.911e-01 -4.981e-03
## shrub_density:siteTecopa:year
## 4.848e-03
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.02358 AIC: -190
anova_results3 <- aov(model3, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## shrub_density 1 0.020430 44 0.285923 8.936e-08 ***
## site 2 0.011457 42 0.274466 0.0003297 ***
## year 1 0.000058 41 0.274408 0.7763951
## aridity 1 0.000022 40 0.274386 0.8618492
## shrub_density:site 2 0.001475 38 0.272911 0.3562435
## shrub_density:year 1 0.000932 37 0.271980 0.2535206
## site:year 2 0.242134 35 0.029845 < 2.2e-16 ***
## shrub_density:site:year 2 0.006265 33 0.023580 0.0124756 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results3)
## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 0.02043 0.02043 28.592 6.62e-06 ***
## site 2 0.01146 0.00573 8.017 0.00145 **
## year 1 0.00006 0.00006 0.081 0.77817
## aridity 1 0.00002 0.00002 0.030 0.86291
## shrub_density:site 2 0.00148 0.00074 1.032 0.36746
## shrub_density:year 1 0.00093 0.00093 1.304 0.26174
## site:year 2 0.24213 0.12107 169.432 < 2e-16 ***
## shrub_density:site:year 2 0.00627 0.00313 4.384 0.02049 *
## Residuals 33 0.02358 0.00071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e4 <- emmeans(model3, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e4 ### Need to double check emmeans it does not match the figure
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.1186 0.0340 33 0.04944 0.188
## Cuyama 2022 0.2302 0.0491 33 0.13028 0.330
## Tecopa 2022 -0.0251 0.0763 33 -0.18038 0.130
## Carrizo 2023 0.0856 0.0310 33 0.02254 0.149
## Cuyama 2023 0.0684 0.0491 33 -0.03150 0.168
## Tecopa 2023 0.1615 0.0763 33 0.00617 0.317
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -0.1116 0.0205 33 -5.440 0.0001
## Carrizo year2022 - Tecopa year2022 0.1437 0.1092 33 1.316 0.7744
## Carrizo year2022 - Carrizo year2023 0.0330 0.0153 33 2.165 0.2809
## Carrizo year2022 - Cuyama year2023 0.0502 0.0205 33 2.446 0.1701
## Carrizo year2022 - Tecopa year2023 -0.0429 0.1092 33 -0.393 0.9987
## Cuyama year2022 - Tecopa year2022 0.2553 0.1246 33 2.048 0.3384
## Cuyama year2022 - Carrizo year2023 0.1446 0.0243 33 5.941 <.0001
## Cuyama year2022 - Cuyama year2023 0.1618 0.0134 33 12.094 <.0001
## Cuyama year2022 - Tecopa year2023 0.0687 0.1246 33 0.551 0.9934
## Tecopa year2022 - Carrizo year2023 -0.1106 0.1056 33 -1.048 0.8980
## Tecopa year2022 - Cuyama year2023 -0.0935 0.1246 33 -0.750 0.9738
## Tecopa year2022 - Tecopa year2023 -0.1865 0.0135 33 -13.861 <.0001
## Carrizo year2023 - Cuyama year2023 0.0172 0.0243 33 0.705 0.9800
## Carrizo year2023 - Tecopa year2023 -0.0759 0.1056 33 -0.719 0.9782
## Cuyama year2023 - Tecopa year2023 -0.0931 0.1246 33 -0.747 0.9743
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### All below in chunk are by site
### Abundance vs Density
abundance <- ggplot(final_data, aes(shrub_density, animals, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance
## `geom_smooth()` using formula = 'y ~ x'
richness <- ggplot(final_data, aes(shrub_density, richness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness", color = "Region")
richness
## `geom_smooth()` using formula = 'y ~ x'
Evenness <- ggplot(final_data, aes(shrub_density, Average_Evenness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
Evenness
## `geom_smooth()` using formula = 'y ~ x'
library(patchwork)
##
## Attaching package: 'patchwork'
##
## The following object is masked from 'package:MASS':
##
## area
density_plot <- abundance/richness/Evenness
density_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Stats for Average Temperature
### Abundance and temperature
model4 <- glm(animals~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model4
##
## Call: glm(formula = animals ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -6.423e+08
## mean_temp
## 2.677e+07
## siteCuyama
## 4.148e+08
## siteTecopa
## -4.661e+08
## year
## 3.175e+05
## mean_humidity
## 2.602e+07
## mean_temp:siteCuyama
## -1.626e+07
## mean_temp:siteTecopa
## 1.146e+07
## mean_temp:year
## -1.323e+04
## siteCuyama:year
## -2.051e+05
## siteTecopa:year
## 2.304e+05
## mean_temp:mean_humidity
## -1.084e+06
## siteCuyama:mean_humidity
## -1.764e+07
## siteTecopa:mean_humidity
## -7.572e+02
## year:mean_humidity
## -1.286e+04
## mean_temp:siteCuyama:year
## 8.037e+03
## mean_temp:siteTecopa:year
## -5.664e+03
## mean_temp:siteCuyama:mean_humidity
## 6.983e+05
## mean_temp:siteTecopa:mean_humidity
## 2.925e+01
## mean_temp:year:mean_humidity
## 5.360e+02
## siteCuyama:year:mean_humidity
## 8.722e+03
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -3.452e+02
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 765800
## Residual Deviance: 57540 AIC: 504.6
anova_results4 <- aov(model4, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model4, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## mean_temp 1 69722 44 696052 6.934e-08
## site 2 82375 42 613676 3.456e-08
## year 1 184771 41 428905 < 2.2e-16
## mean_humidity 1 1138 40 427767 0.4907466
## mean_temp:site 2 108585 38 319182 1.461e-10
## mean_temp:year 1 7837 37 311345 0.0706052
## site:year 2 33153 35 278192 0.0009931
## mean_temp:mean_humidity 1 962 34 277230 0.5264108
## site:mean_humidity 2 5743 32 271487 0.3018453
## year:mean_humidity 1 7165 31 264322 0.0838464
## mean_temp:site:year 2 6207 29 258114 0.2739932
## mean_temp:site:mean_humidity 2 160588 27 97526 2.845e-15
## mean_temp:year:mean_humidity 1 367 26 97159 0.6956019
## site:year:mean_humidity 1 36297 25 60862 9.978e-05
## mean_temp:site:year:mean_humidity 1 3326 24 57536 0.2388432
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity
## mean_temp:site ***
## mean_temp:year .
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity
## year:mean_humidity .
## mean_temp:site:year
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results4)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 69722 69722 28.639 1.50e-05 ***
## site 2 82375 41188 16.919 2.26e-05 ***
## year 1 184771 184771 75.898 4.80e-09 ***
## mean_humidity 1 1138 1138 0.468 0.500364
## mean_temp:site 2 108585 54292 22.301 2.76e-06 ***
## mean_temp:year 1 7837 7837 3.219 0.084892 .
## site:year 2 33153 16577 6.809 0.004358 **
## mean_temp:mean_humidity 1 962 962 0.395 0.535285
## site:mean_humidity 2 5743 2872 1.180 0.323946
## year:mean_humidity 1 7165 7165 2.943 0.098614 .
## mean_temp:site:year 2 6207 3104 1.275 0.297010
## mean_temp:site:mean_humidity 2 160588 80294 32.982 9.74e-08 ***
## mean_temp:year:mean_humidity 1 367 367 0.151 0.701105
## site:year:mean_humidity 1 36297 36297 14.910 0.000707 ***
## Residuals 25 60862 2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e5 <- emmeans(model4, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e5
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 -938.5 619 24 -2217 340
## Cuyama 2022 1714.0 146 24 1414 2015
## Tecopa 2022 -89833.5 55883 24 -205170 25503
## Carrizo 2023 37.5 261 24 -501 576
## Cuyama 2023 1006.2 1997 24 -3116 5128
## Tecopa 2023 -81.9 3323 24 -6940 6776
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -2652 636 24 -4.170 0.0041
## Carrizo year2022 - Tecopa year2022 88895 55335 24 1.606 0.6024
## Carrizo year2022 - Carrizo year2023 -976 672 24 -1.453 0.6959
## Carrizo year2022 - Cuyama year2023 -1945 2091 24 -0.930 0.9347
## Carrizo year2022 - Tecopa year2023 -857 3380 24 -0.253 0.9998
## Cuyama year2022 - Tecopa year2022 91548 55883 24 1.638 0.5828
## Cuyama year2022 - Carrizo year2023 1676 299 24 5.615 0.0001
## Cuyama year2022 - Cuyama year2023 708 2003 24 0.353 0.9992
## Cuyama year2022 - Tecopa year2023 1796 3326 24 0.540 0.9938
## Tecopa year2022 - Carrizo year2023 -89871 55876 24 -1.608 0.6012
## Tecopa year2022 - Cuyama year2023 -90840 55919 24 -1.624 0.5913
## Tecopa year2022 - Tecopa year2023 -89752 57406 24 -1.563 0.6288
## Carrizo year2023 - Cuyama year2023 -969 2014 24 -0.481 0.9964
## Carrizo year2023 - Tecopa year2023 119 3333 24 0.036 1.0000
## Cuyama year2023 - Tecopa year2023 1088 3877 24 0.281 0.9997
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Richness and temperature
model5 <- glm(richness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model5
##
## Call: glm(formula = richness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## 4.250e+05
## mean_temp
## -2.300e+04
## siteCuyama
## 8.940e+06
## siteTecopa
## 1.097e+05
## year
## -2.101e+02
## mean_humidity
## -1.721e+04
## mean_temp:siteCuyama
## -3.999e+05
## mean_temp:siteTecopa
## 5.848e+03
## mean_temp:year
## 1.137e+01
## siteCuyama:year
## -4.421e+03
## siteTecopa:year
## -5.411e+01
## mean_temp:mean_humidity
## 9.181e+02
## siteCuyama:mean_humidity
## -2.453e+05
## siteTecopa:mean_humidity
## 4.210e+00
## year:mean_humidity
## 8.507e+00
## mean_temp:siteCuyama:year
## 1.977e+02
## mean_temp:siteTecopa:year
## -2.896e+00
## mean_temp:siteCuyama:mean_humidity
## 1.097e+04
## mean_temp:siteTecopa:mean_humidity
## -3.693e-02
## mean_temp:year:mean_humidity
## -4.539e-01
## siteCuyama:year:mean_humidity
## 1.213e+02
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -5.424e+00
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 575.9
## Residual Deviance: 21.07 AIC: 140.6
anova_results5 <- aov(model5, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model5, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## mean_temp 1 212.116 44 363.80 < 2.2e-16
## site 2 13.679 42 350.12 0.0004135
## year 1 101.722 41 248.40 < 2.2e-16
## mean_humidity 1 36.989 40 211.41 8.530e-11
## mean_temp:site 2 105.199 38 106.21 < 2.2e-16
## mean_temp:year 1 22.336 37 83.87 4.559e-07
## site:year 2 17.597 35 66.28 4.442e-05
## mean_temp:mean_humidity 1 1.362 34 64.91 0.2129439
## site:mean_humidity 2 6.395 32 58.52 0.0261978
## year:mean_humidity 1 0.241 31 58.28 0.6000577
## mean_temp:site:year 2 7.846 29 50.43 0.0114660
## mean_temp:site:mean_humidity 2 3.530 27 46.90 0.1339519
## mean_temp:year:mean_humidity 1 16.254 26 30.65 1.686e-05
## site:year:mean_humidity 1 8.757 25 21.89 0.0015875
## mean_temp:site:year:mean_humidity 1 0.821 24 21.07 0.3334469
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity *
## year:mean_humidity
## mean_temp:site:year *
## mean_temp:site:mean_humidity
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity **
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results5)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 212.12 212.12 242.238 2.27e-14 ***
## site 2 13.68 6.84 7.811 0.002316 **
## year 1 101.72 101.72 116.168 6.92e-11 ***
## mean_humidity 1 36.99 36.99 42.241 8.30e-07 ***
## mean_temp:site 2 105.20 52.60 60.069 2.83e-10 ***
## mean_temp:year 1 22.34 22.34 25.508 3.27e-05 ***
## site:year 2 17.60 8.80 10.048 0.000627 ***
## mean_temp:mean_humidity 1 1.36 1.36 1.555 0.223910
## site:mean_humidity 2 6.39 3.20 3.652 0.040617 *
## year:mean_humidity 1 0.24 0.24 0.276 0.604211
## mean_temp:site:year 2 7.85 3.92 4.480 0.021736 *
## mean_temp:site:mean_humidity 2 3.53 1.76 2.015 0.154342
## mean_temp:year:mean_humidity 1 16.25 16.25 18.563 0.000224 ***
## site:year:mean_humidity 1 8.76 8.76 10.000 0.004075 **
## Residuals 25 21.89 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e6 <- emmeans(model5, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e6
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 12.10 11.85 24 -12.360 36.56
## Cuyama 2022 1.88 2.80 24 -3.898 7.67
## Tecopa 2022 161.29 1069.41 24 -2045.856 2368.43
## Carrizo 2023 10.67 4.99 24 0.371 20.97
## Cuyama 2023 142.87 38.22 24 63.983 221.75
## Tecopa 2023 33.32 63.59 24 -97.914 164.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 10.22 12.17 24 0.839 0.9570
## Carrizo year2022 - Tecopa year2022 -149.19 1058.93 24 -0.141 1.0000
## Carrizo year2022 - Carrizo year2023 1.43 12.86 24 0.111 1.0000
## Carrizo year2022 - Cuyama year2023 -130.77 40.02 24 -3.268 0.0340
## Carrizo year2022 - Tecopa year2023 -21.22 64.68 24 -0.328 0.9994
## Cuyama year2022 - Tecopa year2022 -159.41 1069.41 24 -0.149 1.0000
## Cuyama year2022 - Carrizo year2023 -8.79 5.71 24 -1.538 0.6444
## Cuyama year2022 - Cuyama year2023 -140.98 38.32 24 -3.679 0.0133
## Cuyama year2022 - Tecopa year2023 -31.44 63.65 24 -0.494 0.9959
## Tecopa year2022 - Carrizo year2023 150.62 1069.27 24 0.141 1.0000
## Tecopa year2022 - Cuyama year2023 18.42 1070.09 24 0.017 1.0000
## Tecopa year2022 - Tecopa year2023 127.97 1098.55 24 0.116 1.0000
## Carrizo year2023 - Cuyama year2023 -132.19 38.54 24 -3.430 0.0236
## Carrizo year2023 - Tecopa year2023 -22.65 63.78 24 -0.355 0.9992
## Cuyama year2023 - Tecopa year2023 109.55 74.19 24 1.477 0.6816
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Evenness and temperature
model6 <- glm(Average_Evenness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model6
##
## Call: glm(formula = Average_Evenness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -2.924e+04
## mean_temp
## 1.075e+03
## siteCuyama
## 1.978e+05
## siteTecopa
## 1.918e+04
## year
## 1.445e+01
## mean_humidity
## 1.089e+03
## mean_temp:siteCuyama
## -8.739e+03
## mean_temp:siteTecopa
## -7.814e+02
## mean_temp:year
## -5.312e-01
## siteCuyama:year
## -9.782e+01
## siteTecopa:year
## -9.486e+00
## mean_temp:mean_humidity
## -3.940e+01
## siteCuyama:mean_humidity
## -6.112e+03
## siteTecopa:mean_humidity
## -1.112e-02
## year:mean_humidity
## -5.383e-01
## mean_temp:siteCuyama:year
## 4.321e+00
## mean_temp:siteTecopa:year
## 3.864e-01
## mean_temp:siteCuyama:mean_humidity
## 2.686e+02
## mean_temp:siteTecopa:mean_humidity
## 5.490e-04
## mean_temp:year:mean_humidity
## 1.948e-02
## siteCuyama:year:mean_humidity
## 3.022e+00
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -1.328e-01
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.001288 AIC: -305.7
anova_results6 <- aov(model6, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model6, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## mean_temp 1 0.011046 44 0.295307 < 2.2e-16
## site 2 0.073313 42 0.221994 < 2.2e-16
## year 1 0.068416 41 0.153577 < 2.2e-16
## mean_humidity 1 0.004958 40 0.148620 < 2.2e-16
## mean_temp:site 2 0.112108 38 0.036512 < 2.2e-16
## mean_temp:year 1 0.002065 37 0.034447 5.578e-10
## site:year 2 0.012030 35 0.022417 < 2.2e-16
## mean_temp:mean_humidity 1 0.000145 34 0.022272 0.1001135
## site:mean_humidity 2 0.000774 32 0.021498 0.0007366
## year:mean_humidity 1 0.001450 31 0.020048 2.032e-07
## mean_temp:site:year 2 0.003008 29 0.017040 6.819e-13
## mean_temp:site:mean_humidity 2 0.005407 27 0.011633 < 2.2e-16
## mean_temp:year:mean_humidity 1 0.007064 26 0.004569 < 2.2e-16
## site:year:mean_humidity 1 0.002788 25 0.001781 5.712e-13
## mean_temp:site:year:mean_humidity 1 0.000492 24 0.001288 0.0024604
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity ***
## year:mean_humidity ***
## mean_temp:site:year ***
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results6)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 0.01105 0.01105 155.089 3.23e-12 ***
## site 2 0.07331 0.03666 514.655 < 2e-16 ***
## year 1 0.06842 0.06842 960.555 < 2e-16 ***
## mean_humidity 1 0.00496 0.00496 69.605 1.08e-08 ***
## mean_temp:site 2 0.11211 0.05605 786.988 < 2e-16 ***
## mean_temp:year 1 0.00206 0.00206 28.990 1.38e-05 ***
## site:year 2 0.01203 0.00602 84.452 7.58e-12 ***
## mean_temp:mean_humidity 1 0.00015 0.00015 2.038 0.165804
## site:mean_humidity 2 0.00077 0.00039 5.437 0.010954 *
## year:mean_humidity 1 0.00145 0.00145 20.352 0.000132 ***
## mean_temp:site:year 2 0.00301 0.00150 21.114 4.26e-06 ***
## mean_temp:site:mean_humidity 2 0.00541 0.00270 37.958 2.66e-08 ***
## mean_temp:year:mean_humidity 1 0.00706 0.00706 99.178 3.48e-10 ***
## site:year:mean_humidity 1 0.00279 0.00279 39.150 1.52e-06 ***
## Residuals 25 0.00178 0.00007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e7 <- emmeans(model6, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e7
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.2333 0.0927 24 0.0420 0.4246
## Cuyama 2022 -0.0603 0.0218 24 -0.1054 -0.0153
## Tecopa 2022 -0.0817 8.3624 24 -17.3409 17.1775
## Carrizo 2023 0.0399 0.0390 24 -0.0407 0.1204
## Cuyama 2023 2.0244 0.2989 24 1.4075 2.6413
## Tecopa 2023 -0.0993 0.4972 24 -1.1255 0.9269
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 0.2937 0.0952 24 3.084 0.0508
## Carrizo year2022 - Tecopa year2022 0.3150 8.2805 24 0.038 1.0000
## Carrizo year2022 - Carrizo year2023 0.1935 0.1005 24 1.924 0.4125
## Carrizo year2022 - Cuyama year2023 -1.7911 0.3129 24 -5.724 0.0001
## Carrizo year2022 - Tecopa year2023 0.3326 0.5058 24 0.658 0.9849
## Cuyama year2022 - Tecopa year2022 0.0213 8.3625 24 0.003 1.0000
## Cuyama year2022 - Carrizo year2023 -0.1002 0.0447 24 -2.241 0.2565
## Cuyama year2022 - Cuyama year2023 -2.0848 0.2997 24 -6.957 <.0001
## Cuyama year2022 - Tecopa year2023 0.0390 0.4977 24 0.078 1.0000
## Tecopa year2022 - Carrizo year2023 -0.1215 8.3614 24 -0.015 1.0000
## Tecopa year2022 - Cuyama year2023 -2.1061 8.3678 24 -0.252 0.9998
## Tecopa year2022 - Tecopa year2023 0.0176 8.5903 24 0.002 1.0000
## Carrizo year2023 - Cuyama year2023 -1.9846 0.3014 24 -6.584 <.0001
## Carrizo year2023 - Tecopa year2023 0.1392 0.4987 24 0.279 0.9997
## Cuyama year2023 - Tecopa year2023 2.1237 0.5801 24 3.661 0.0139
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Ambient Temperature Plots
### Plots of temp might look super ugly.
abundance_temp <- ggplot(final_data, aes(mean_temp, animals)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") +
labs(x = "Average Temperature (C)", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")
abundance_temp
## `geom_smooth()` using formula = 'y ~ x'
richness_temp <- ggplot(final_data, aes(mean_temp, richness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme(axis.title.x = element_blank()) + labs(tag = "B")+ theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Average Temperature (C)", y = "Richness") + theme(axis.title.x = element_blank())
richness_temp
## `geom_smooth()` using formula = 'y ~ x'
evenness_temp <- ggplot(final_data, aes(mean_temp, Average_Evenness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Mean Temperature (°C)"), y = "Mean Evenness")
evenness_temp
## `geom_smooth()` using formula = 'y ~ x'
temp_plot <- abundance_temp/richness_temp/evenness_temp
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Other posisble figure options
abundance_2 <- ggplot(final_data, aes(shrub_density, animals)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance_2
## `geom_smooth()` using formula = 'y ~ x'
richness_2 <- ggplot(final_data, aes(shrub_density, richness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness")
richness_2
## `geom_smooth()` using formula = 'y ~ x'
evenness_2 <- ggplot(final_data, aes(shrub_density, Average_Evenness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
plot1.1 <- abundance_2/richness_2/evenness_2
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Percent proportion Figure
#write.csv(density_obvs_final, file = "Animal Observations.csv")
scientific_names <- read.csv("Animal Observations.csv")
plot3 <- ggplot(scientific_names, aes(scientific_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot3 <-plot3 + scale_fill_manual(values = c("#009900", "#0066cc"))
# Assuming you have a dataset named scientific_names with columns: scientific_name, percent_presence, microsite
# Create a new variable to specify the ordering based on presence in density or open areas
scientific_names$microsite <- ifelse(scientific_names$microsite == "Density", "Shrub", scientific_names$microsite)
scientific_names <- scientific_names %>%
mutate(ordering_var = ifelse(microsite == "Shrub", 1, 2)) %>%
arrange(ordering_var, desc(percent_presence))
# Create the histogram-style figure with reordering
plot3.2 <- ggplot(scientific_names, aes(fct_inorder(scientific_name), percent_presence, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Percent Proportion") +
labs(fill = "Microsite") +
scale_fill_manual(values = c("#0066cc", "#009900")) + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))
# Reorder the x-axis labels
plot3.2 <- plot3.2 + scale_x_discrete(limits = rev(levels(scientific_names$scientific_name)))
# Plot the figure
print(plot3.2)
### Figure 1: Abundance, Richness, Evenness vs Shrub Density
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 2: Abundance, Richness, Evenness vs Average Temperature
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 3: PCOA of Communities
pcoa_final
### Figure 4: Percent Proportion of Vertebrate species
plot3.2
#write.csv(final_data, file = "Final Data.csv")
### Test figure with formula (Shrub Density)
final_data <- final_data %>%
mutate(year = as.character(year))
### Density v abundance
ggplot(final_data, aes(shrub_density, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Density v Richness
ggplot(final_data, aes(shrub_density, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Density v Evenness
ggplot(final_data, aes(shrub_density, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Aridity v Abundance
ggplot(final_data, aes(aridity, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Aridity v Richness
ggplot(final_data, aes(aridity, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Aridity v Evenness
ggplot(final_data, aes(aridity, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Stats
#install.packages("lme4", type = "source")
library(lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(performance)
#simple
m1 <- lmer(animals ~ shrub_density + (1|year) + aridity, data = final_data)
check_collinearity(m1)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m1)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 14370 14370 1 42.018 1.2164 0.27635
## aridity 114226 114226 1 42.010 9.6692 0.00336 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeansLT(m1)
## Least Squares Means table:
##
## Estimate Std. Error df t value lower upper Pr(>|t|)
##
## Confidence level: 95%
## Degrees of freedom method: Satterthwaite
ranova(m1) # I think this is to test for random effects across year?
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## animals ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -271.70 553.40
## (1 | year) 4 -274.97 557.94 6.5368 1 0.01057 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m2 <- lmer(richness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m2)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m2)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 17.096 17.096 1 43 1.8397 0.1820664
## aridity 149.184 149.184 1 43 16.0534 0.0002401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m2)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## richness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -116.83 243.66
## (1 | year) 4 -116.83 241.66 -8.5265e-14 1 1
m3 <- lmer(Average_Evenness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m3)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m3)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 0.0215532 0.0215532 1 43 3.2712 0.0775 .
## aridity 0.0026079 0.0026079 1 43 0.3958 0.5326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m3)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## Average_Evenness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 39.082 -68.164
## (1 | year) 4 39.082 -70.164 -1.4211e-14 1 1
trophic <- read.csv("Animal Observations.csv")
ggplot(trophic, aes(microsite, captures, color = Trophic)) +
geom_boxplot() +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
m9<- glm(total ~ microsite * Trophic, family = "poisson", data = trophic)
anova(m9, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## Trophic 2 7884.0 52 19907 < 2.2e-16 ***
## microsite:Trophic 2 2.0 50 19905 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e9 <- emmeans(m9, pairwise~microsite|Trophic)
e9
## $emmeans
## Trophic = Carnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0767 Inf 2.91 3.21
## Open 3.18 0.0769 Inf 3.03 3.33
##
## Trophic = Herbivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 5.62 0.0173 Inf 5.59 5.66
## Open 5.71 0.0174 Inf 5.68 5.74
##
## Trophic = Omnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0685 Inf 2.92 3.19
## Open 3.00 0.0788 Inf 2.85 3.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## Trophic = Carnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.1276 0.1086 Inf -1.175 0.2400
##
## Trophic = Herbivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.0849 0.0245 Inf -3.461 0.0005
##
## Trophic = Omnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open 0.0567 0.1044 Inf 0.543 0.5869
##
## Results are given on the log (not the response) scale.
final_data_2022 <- final_data %>%
filter(year == "2022")
shapiro.test(final_data_2022$animals)
##
## Shapiro-Wilk normality test
##
## data: final_data_2022$animals
## W = 0.76017, p-value = 7.037e-05
ggqqplot(final_data_2022$animals)
n1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n1
##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -27.7891 0.6522 54.0811
## shrub_density:aridity
## 1.6648
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 376700 AIC: 310
anova(n1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2314117
## aridity 1 211738 21 381211 0.0008004 ***
## shrub_density:aridity 1 4465 20 376746 0.6263640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n1), "\n")
## Linear Model AIC: 309.9796
quadratic_model_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 139.405 100.932 -9.768
## aridity I(aridity^2) shrub_density:aridity
## -412.549 117.917 6.412
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 186500 AIC: 297.1
anova(quadratic_model_1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.106605
## I(shrub_density^2) 1 2942 21 590007 0.594141
## aridity 1 261283 20 328724 5.12e-07 ***
## I(aridity^2) 1 89956 19 238768 0.003213 **
## shrub_density:aridity 1 52272 18 186496 0.024696 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_1), "\n")
## Quadratic Model AIC: 297.1038
if (AIC(quadratic_model_1) < AIC(n1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
abund_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, animals)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Richness
n2 <- glm(richness ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n2
##
## Call: glm(formula = richness ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -0.34946 0.08662 2.58054
## shrub_density:aridity
## -0.01174
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 475
## Residual Deviance: 132.4 AIC: 119.1
anova(n2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.43 22 468.53 0.3243
## aridity 1 335.94 21 132.58 1.043e-12 ***
## shrub_density:aridity 1 0.22 20 132.36 0.8546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n2), "\n")
## Linear Model AIC: 119.0888
quadratic_model_2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_2
##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 1.9865 -0.7054 0.0776
## aridity I(aridity^2) shrub_density:aridity
## -3.8810 1.6566 -0.0559
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 475
## Residual Deviance: 87.2 AIC: 113.1
anova(quadratic_model_2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.431 22 468.53 0.24925
## I(shrub_density^2) 1 106.430 21 362.10 2.769e-06 ***
## aridity 1 247.045 20 115.05 9.248e-13 ***
## I(aridity^2) 1 23.883 19 91.17 0.02639 *
## shrub_density:aridity 1 3.972 18 87.20 0.36517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
rich_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, richness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Evenness
n3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
n3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.019618 -0.001800 0.030276
## shrub_density:aridity
## 0.001651
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.05188 AIC: -69.18
anova(n3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.0927 .
## aridity 1 0.081917 21 0.056269 1.913e-08 ***
## shrub_density:aridity 1 0.004391 20 0.051878 0.1932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n3), "\n")
## Linear Model AIC: -69.17676
quadratic_model_3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_3
##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.0446964 -0.0295177 0.0027085
## aridity I(aridity^2) shrub_density:aridity
## -0.0385879 0.0178654 0.0002091
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.03225 AIC: -76.59
anova(quadratic_model_3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.04307 *
## I(shrub_density^2) 1 0.056383 21 0.081804 2.027e-08 ***
## aridity 1 0.047100 20 0.034703 2.941e-07 ***
## I(aridity^2) 1 0.002397 19 0.032306 0.24743
## shrub_density:aridity 1 0.000056 18 0.032251 0.86020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
even_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
plot <- abund_dens_2022/rich_dens_2022/even_dens_2022
plot
### Temperature (Hold off on this and ask chris)
t1 <- glm(animals ~ shrub_density * mean_temp, family = "gaussian", data = final_data_2022)
t1
##
## Call: glm(formula = animals ~ shrub_density * mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density mean_temp
## 641.3669 22.3664 -20.8447
## shrub_density:mean_temp
## -0.6221
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 389100 AIC: 310.8
anova(t1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.238985
## mean_temp 1 199846 21 393103 0.001351 **
## shrub_density:mean_temp 1 3970 20 389134 0.651496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(t1), "\n")
## Linear Model AIC: 310.756
quadratic_temp_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", data = final_data_2022)
quadratic_temp_1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 4363.574 155.190 -6.801
## mean_temp I(mean_temp^2) shrub_density:mean_temp
## -305.916 5.317 -2.618
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 301200 AIC: 308.6
anova(quadratic_temp_1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2042037
## I(shrub_density^2) 1 2942 21 590007 0.6750263
## mean_temp 1 240180 20 349827 0.0001516 ***
## I(mean_temp^2) 1 2772 19 347055 0.6840117
## shrub_density:mean_temp 1 45820 18 301235 0.0979909 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_temp_1), "\n")
## Quadratic Model AIC: 308.6113
final_data_2023 <- final_data %>%
filter(year == "2023")
x1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2023)
x1
##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 8.32431 1.85904 2.30955
## shrub_density:aridity
## -0.05488
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 12650
## Residual Deviance: 10190 AIC: 207.5
anova(x1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12649
## shrub_density 1 2257.72 20 10391 0.04579 *
## aridity 1 199.04 19 10192 0.55317
## shrub_density:aridity 1 4.64 18 10187 0.92782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x1), "\n")
## Linear Model AIC: 207.4663
quadratic_model_x1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2023)
quadratic_model_x1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## -1.65760 3.43942 -0.15541
## aridity I(aridity^2) shrub_density:aridity
## 29.97243 -7.06338 0.03991
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 12650
## Residual Deviance: 9619 AIC: 210.2
anova(quadratic_model_x1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12648.8
## shrub_density 1 2257.72 20 10391.1 0.05264 .
## I(shrub_density^2) 1 55.46 19 10335.6 0.76135
## aridity 1 317.46 18 10018.1 0.46743
## I(aridity^2) 1 396.75 17 9621.4 0.41659
## shrub_density:aridity 1 1.93 16 9619.5 0.95482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x1), "\n")
## Quadratic Model AIC: 210.2043
if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
abund_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, animals)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
abund_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
x2 <- glm(richness ~ shrub_density * aridity, data = final_data_2023)
x2
##
## Call: glm(formula = richness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 4.775057 0.220144 -0.167510
## shrub_density:aridity
## -0.009058
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 95.32
## Residual Deviance: 67.28 AIC: 97.03
anova(x2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.009006 **
## aridity 1 2.4110 19 67.409 0.421898
## shrub_density:aridity 1 0.1265 18 67.282 0.854019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x2), "\n")
## Linear Model AIC: 97.02606
quadratic_model_x2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x2
##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 3.32139 -0.05358 0.02657
## aridity I(aridity^2) shrub_density:aridity
## 3.87515 -1.02486 -0.02127
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 95.32
## Residual Deviance: 59.25 AIC: 98.23
anova(quadratic_model_x2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.008689 **
## I(shrub_density^2) 1 0.4031 19 69.417 0.741448
## aridity 1 2.0180 18 67.399 0.460389
## I(aridity^2) 1 7.6014 17 59.797 0.151936
## shrub_density:aridity 1 0.5479 16 59.249 0.700486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x2), "\n")
## Quadratic Model AIC: 98.22897
if (AIC(quadratic_model_x2) < AIC(x2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
rich_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, richness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
rich_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
x3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
x3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.1816823 0.0084948 -0.0469045
## shrub_density:aridity
## -0.0009284
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.009233 AIC: -98.64
anova(x3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 2.33e-07 ***
## aridity 1 0.136519 19 0.010562 < 2.2e-16 ***
## shrub_density:aridity 1 0.001329 18 0.009233 0.1074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x3), "\n")
## Linear Model AIC: -98.63904
quadratic_model_x3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x3
##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.1963905 0.0105327 -0.0001974
## aridity I(aridity^2) shrub_density:aridity
## -0.0877876 0.0103750 -0.0008426
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.008405 AIC: -96.71
anova(quadratic_model_x3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 3.226e-07 ***
## I(shrub_density^2) 1 0.020159 19 0.126922 5.833e-10 ***
## aridity 1 0.116561 18 0.010361 < 2.2e-16 ***
## I(aridity^2) 1 0.001096 17 0.009265 0.1485
## shrub_density:aridity 1 0.000860 16 0.008405 0.2006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x3), "\n")
## Quadratic Model AIC: -96.7066
if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
even_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
even_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
plot2 <- abund_dens_2023/rich_dens_2023/even_dens_2023
plot2
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'